Data warehousing and mining concepts, methodologies, tools, and applications 1
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Information Science Reference
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| adam_text | Contents
by
Volume
Volume I
Section
1.
Fundamental Concepts and Theories
This section serves as the foundation for this exhaustive reference tool by addressing crucial theories
essential to the understanding of data mining and warehousing. Chapters found within these pages
provide an excellent framework in which to position data mining and warehousing within the field of
information science and technology, individual contributions provide insight into the critical incorpo¬
ration of data mining and warehousing in the global community and explore crucial stumbling blocks
of this field. Within this introductory section, the reader can learn and choose from a compendium of
expert research on the elemental theories underscoring the research and application of data mining
and warehousing.
Chapter
1.1.
Administering and Managing a Data Warehouse
/
James E. Yao, Chang Liu, QiyangChen, and June
Lu
.........................................................................1
Chapter
1.2.
Knowledge Structure and Data Mining Techniques
/
Rick L. Wilson,
Peter A. Rosen, and Mohammad SaadAl-Ahmadi
...............................................................................9
Chapter
1.3.
Physical Data Warehousing Design
/
Ladjel Bellatreche and Mukesh Mohania
........................................................................................... 18
Chapter
1.4.
Introduction to Data Mining Techniques via Multiple Criteria Optimization
Approaches and Applications
/
Yong Shi, Yi Peng, Gang
Кои,
and Zhengxin Chen
..........................26
Chapter
1.5.
Privacy-Preserving Data Mining on the Web: Foundations and Techniques
/
Stanley R. M.
Oliveira
and
Osmar
R.
Zafane
.....................................................................................50
Chapter
1.6.
Multi-Label Classification: An Overview
/
Grigorios Tsoumakas and
Loamtis Katakis
..................................................................................................................................64
Chapter
1.7.
Online Data Mining
/
Héctor
Oscar Nigro and
Sandra Elizabeth Gonzalez
Cisara
.....................................................................................................75
Chapter
1.8.
A Look Back at the PAKDD Data Mining Competition
2006 /
Nathaniel B. Nortel and Chew
Lim Tan..............................................................................................84
Chapter
1.9.
Introduction to Data Mining in Bioinformatics
/
Hui-Hnang Hsu
.................................93
Chapter
1.10.
Algorithmic Aspects of Protein Threading/ Tatsuya Akutsu
......................................103
Chapter
1.11.
A Tutorial on Hierarchical Classification with Applications in
Bioinformatics I Alex
Freitas
and
André
C.P.L.F.
de Carvalho.........................................................
119
Chapter
1.12.
Introduction to Data Mining and its Applications to Manufacturing
/
Jose D.
Montero
................................................................................................................................146
Chapter
1.13.
Data Warehousing and
OLAP/ Jose
Hernandez-Orallo
............................................169
Chapter
1.14.
Data Warehousing, Multi-Dimensional Data Models and
OLAP/
Prasad
M.
Deshpande and Karthikeyan Ramasamy
........................................................................179
Chapter
1.15.
A Literature Overview of Fuzzy Database Modeling/
Z
M. Ma
...............................187
Chapter
1.16.
Conceptual Modeling Solutions for the Data Warehouse
/ Stefano
Rizzi..................
208
Chapter
1.17.
Pattern Comparison in Data Mining: A Survey
/
Irene Ntoutsi,
Nikos Pelekis, and Yannis Theodoridis
.............................................................................................228
Chapter
1.18.
Pattern Mining and Clustering on Image Databases
/
Marmette
BoueU
Pierre
Gançarski,
and
Marie-Aude
Anfaure, and Omar Boussaid
...................................................254
Chapter
1.19.
Conceptual Data Modeling Patterns: Representation
and Validation
/
Dinesh Batra
...........................................................................................................280
Chapter
1.20.
Mining Association Rules in Data Warehouses
/
Haorianto Cokrowijoyo Tjioe and David Tuniar
..............................................................................303
Chapter
1.21.
Exception Rules in Data Mining
/
Olena Daly and
Danid
Taniar
.............................336
Chapter
1.22.
Process-Based Data Mining/ Karim K. Hirji
............................................................343
Chapter
1.23.
Integration of Data Sources through Data Mining
/
Andreas Koeller
........................350
Chapter
1.24.
Ensemble Data Mining Methods
/
Nikunj C. Oza
......................................................356
Chapter
1.25.
Evaluation of Data Mining Methods
/
Paolo Giudici
................................................364
Chapter
1.26.
Discovering an Effective Measure in Data Mining/
Tukao
Ito..................................
371
Chapter
1.27.
Data Warehousing and Data Mining Lessons and EC Companies
/
Neerja Sethi and
Vij
ay Sethi
..............................................................................................................381
Chapter
1.28.
Best Practices in Data Warehousing from the Federal
Perspective/
Les Pang
......................................................................................................................389
Chapter
1.29.
Decision Support and Data Warehousing: Challenges of a Global
Information Environment/Alexander Anisimo
v
..............................................................................397
Chapter
1.30.
An Experimental Replication with Data Warehouse Metrics
/
Manuel Serrano, Coral
Calero,
and Mario
Piattini
.........................................................................408
Chapter
1.31.
Data Warehousing Solutions for Reporting Problems
/
Juha Kontio
.........................429
Section
2.
Development and Design Methodologies
This section provides in-depth coverage of conceptual architecture, enabling the reader to gain a com¬
prehensive
unders
landing of the emergingtechnologicaldevelopments within thefieldof dataminingand
warehousing. Research fundamentals imperative to the understanding of developmental processes within
information management are offered. From broad examinations to specific discussions on electronic
tools, the research found within this section spans the discipline while also offering detailed, specific
discussions. Basic designs, as well as abstract developments, are explained within these chapters, and
frameworks for implementing secure data warehouses are explored.
Chapter
2.1.
A Multi-Agent Approach to Collaborative Knowledge Production
/
Juan Manuel Dodero,
Paloma Díaz,
and
Ignacio
Aedo...................................................................
438
Chapter
2.2.
A Framework for Organizational Data Analysis and Organizational
Data Mining
/ Bernd
Knobloc
h
.........................................................................................................449
Chapter
2.3.
Rule-Based Parsing for Web Data Extraction
/
David
Camacho,
Ricardo
A¡er,
and
Juan Cuadrado
....................................................................................................469
Chapter
2.4.
Conceptual and Systematic Design Approach for XML Document Warehouses
/
Vicky Nassis, R. Rajugan, Tharam S. Dillon, and Wenny Rahayu
....................................................485
Chapter
2.5.
A Framework for Efficient Association Rule Mining in XML Data
/
Ji Zhang, Han Liu.
Tok
Wang Ling. Robert M. Bruckner, and
A. Min Tjoa
.....................................509
Chapter
2.6.
A Methodology for Building XML Data Warehouses
/
Laura
Irina
Rusu,
J.
Wenny Rahayu. and David Taniar
.................................................................................................530
Chapter
2.7.
Applying UML for Modeling the Physical Design of Data Warehouses
/
Sergio
Lujan-Mora
and Juan Trujillo
...............................................................................................556
Volume II
Chapter
2.8.
Physical Modeling of Data Warehouses Using UML Component and
Deployment Diagrams: Design and Implementation Issues
/
Sergio Lujan-Mora
and Juan Trujillo
...............................................................................................................................591
Chapter
2.9.
GeoCache: A Cache for GML Geographical Data
/
Lionel Savary,
Georges Gardarin, and
Karine
Zeitouni
...........................................................................................622
Chapter
2.10.
A Java Technology Based Distributed Software Architecture for Web
Usage Mining
/
Juan M. Hernansáez, Juan A. Bolia,
and Antonio EG. Skarmeta
..........................642
Chapter
2.11.
Spatial Data Warehouse Modelling
/
Maria
Luisa Damiani
and
Stefano
Spaccapietra
........................................................................................................................659
Chapter
2.12.
Designing Secure Data Warehouses
/
Rodolfo
Villarroel,
Eduardo Fernández-Medina,
Juan Trujillo, and Mario
Piattini
......................................................679
Chapter
2.13.
Privacy-Preserving Data Mining: Development and Directions
/
Bhavuni Thuraisingham
....................................................................................................................693
Chapter
2.14.
A Service Discovery Model for Mobile Agent-Based Distributed
Data Mining
/
Xining Li, Lei Song
....................................................................................................705
Chapter
2.15.
Node Partitioned Data Warehouses: Experimental Evidence and
Improvements
/
Pedro
Furtado
.........................................................................................................718
Chapter
2.16.
Managing Late Measurements in Data Warehouses
/
Matteo Golfarelli and
Stefano Rizzi..................................................................................................
738
Chapter
2.17.
Toward a Grid-Based Zero-Latency Data Warehousing
Implementation for Continuous Data Streams Processing
/
Tho Manh Nguyen,
Peter Brezany,
A. Min Tjoa,
and Edgar Weippl
................................................................................755
Chapter
2.18.
Data Warehouse Design to Support Customer Relationship
Management Analyses
/
Colleen Cunningham, Il-Yeol Song, and Peter P. Chen
............................787
Chapter
2.19.
An Information-Theoretic Framework for Process Structure and
Data Mining
/
Gianluigi Greco, Antonella
Guzzo,
and
Luigi Pontieri.............................................
810
Chapter
2.20.
Domain-Driven Data
Mining: A
Practical Methodology
/
Longhing
Cao
and Chengqi Zhang
...................................................................................................93
j
Chapter
2.21.
Metric Methods in Data Mining I Dan A. Simovici
....................................................849
Chapter
2.22.
Mining Geo-Referenced Databases: A Way to Improve Decision-Making
/
Maribel Yasmina Santos and Luis Alfredo
Amaral
...........................................................................880
Chapter
2.23.
Ontology-Based Construction of Grid Data Mining Workflows
/
Peter Brezany, Ivan Janeiak, and
A. Min Tjoa
.................................................................................913
Chapter
2.24.
Exploratory Time Series Data Mining by Genetic Clustering
/
T. Warren Liao
...................................................................................................................................942
Chapter
2.25.
Two Rough Set Approaches to Mining Hop Extraction Data/
Jerzy W.
Grzymala-Busse
,
Zdzisław
S.
Hippe,
Teresa Mroczek, Edward
Roj,
and Bolesław Skowronski
..................................................................................................................963
Chapter
2.26.
Semantics-Aware Advanced
OLAP
Visualization of Multidimensional
Data Cubes I Alfredo Cuzzocrea,
Domenico Sacca,
and Paolo
Serafino
.........................................974
Chapter
2.27.
A Presentation Model and Non-Traditional Visualization for
OLAP
/
Andreas Maniatis,
Panos
Vassiliadis, Spiros Skiadopoulos, Yunnis Vassiliou,
George Mavrogonatos, and
Ilias
Michalarias
................................................................................1004
Chapter
2.28.
An Ontology-Based Data Mediation Framework for Semantic
Environments
/
Adrian
Mocan
and Emilia
С
impian
...................................................................... 1037
Chapter
2.29.
Engineering Conceptual Data Models from Domain Ontologies:
A Critical Evaluation
/
Haya
El-Ghalayini, Mohammed Odeh, and Richard McClatchey
............1068
Chapter
2.30.
Data Mining of Bayesian Network Structure Using a Semantic Genetic
Algorithm-Based Approach I
Sachin Shetty, Min Song,
and
Mansoor
Alam.................................
1081
Chapter
2.31.
A Bayesian Framework for Improving Clustering Accuracy of
Protein Sequences Based on Association Rules
/
Peng- Yeng Yin, Shyong-Jian Shyu,
Guan-Shieng Huang, andShuang-Te Liao
.....................................................................................1091
Chapter
2.32.
Improving Classification Accuracy of Decision Trees for Different
Abstraction Levels of Data/
Mina
Jeong and Doheon Lee
.............................................................1103
Chapter
2.33.
Improving Similarity Search in Time Series Using Wavelets
/
loannis Liabotis, Babis Theodoulidis, and Mohamad Saraee
.........................................................
Π
16
Chapter
2.34.
Cluster-Based Input Selection for
Transparant
Fuzzy Modeling
/
Can Yang,
Jun
Meng,
andShanan Zhu
............................................................................................1138
Chapter
2.35.
Combinatorial Fusion Analysis: Methods and Practices of Combining
Multiple Scoring Systems
/
D. Frank Hsu, Yun-Sheng Chung, and Bruce S.
Kristal
.....................1157
Chapter
2.36.
Databases Modeling of Engineering Information
/Ζ.
M. Ma
...................................
1
182
Volume
III
Chapter
2.37.
Novel Efficient Classifiers Based on Data Cube
/
Lixin
Fu
.....................................1205
Chapter
2.38.
Partially Supervised Classification: Based on Weighted
Unlabeled Samples Support Vector Machine
/
ZhigangLiu, Wenzhong Shi,
Deren
Li, andQianqing Qin
...........................................................................................................1216
Chapter
2.39.
Periodic Streaming Data Reduction Using Flexible Adjustment of
Time Section Size
/
Jae
hoon Kim
andSeog Park
..........................................................................1231
Chapter
2.40.
Hybrid Query and Data Ordering for Fast and Progressive
Range-Aggregate Query Answering
/
Cyrus Shahabi, Mehrdad Jahangiri, and
Dimitris
Sacharíais
.........................................................................................................................1250
Chapter
2.41.
Linguistic Rule Extraction from Support Vector Machine Classifiers
/
XiujuFu,
Lipo
Wang, GihGuang Hung, and
Liping Goh
...............................................................1269
Chapter
2.42.
Preference-Based Frequent Pattern Mining
/
Moonjung Cho,
Jian Pei,
Haixun Wang, and Wei Wang
..........................................................................................................1280
Section
3.
Tools and Technologies
This section presents extensive coverage of the interaction between data mining and warehousing and
various tools and technologies that researchers, practitioners, and students alike can implement in their
daily lives. These chapters educate readers about fundamental tools such as the Internet and mobile
technology, while also providing insight into new and upcoming technologies, theories, and instruments
that will soon be commonplace. Within these rigorously researched chapters, readers are presented with
countless examples of the tools and technologies essential to the field of data mining and warehousing.
In addition, the successful implementation and resulting impact of these various tools and technologies
are discussed within this collection of chapters.
Chapter
3.1.
Algorithms for Data Mining
/
Tadao Tukaoka, Nigel K. LI. Pope,
and Kevin E. Voges
.........................................................................................................................1301
Chapter
3.2.
Super Computer Heterogeneous Classifier Meta-Ensembles
/
Anthony
Bagnali,
Gavin Cawley, Ian Whittley, Larry Bull, Matthew Studley,
Mike Pettipher, and
Firat Tekiner
...................................................................................................1320
Chapter
3.3.
Navigation Rules for Exploring Large Multidimensional Data Cubes
/
Navin Kumar, Aryya Gangopadhyay, George Karabatis, Sanjay Bapna,
andZhiyuan Chen
...........................................................................................................................
J334
Chapter
3.4.
The Use of Smart Tokens in Cleaning Integrated Warehouse Data
/
Christie I. Ezeife and Wnothy
E.
Ohanekwu
..................................................................................
J355
Chapter
3.5.
An Implemented Representation and Reasoning System for Creating and
Exploiting Large Knowledge Bases of Narrative Information
/
Gian Piero
larri
.....................1376
Chapter
3.6.
Spatio-Temporal
Prediction Using Data Mining Tools
/
Margaret H. Dunham, Nathaniel Ayewah, Zhigang Li, Kathryn Bean, and Me Huang
.................1400
Chapter
3.7.
Data Mining Using Qualitative Information on the Web
/
Taeho Hong and Woojong
Suh
........................................................................................................1416
Chapter
3.8.
Computational Intelligence Techniques Driven Intelligent
Agents for Web Data Mining and Information Retrieval
/
MasoudMohummadian
and
Rie
Jentzsch
..............................................................................................................................1435
Chapter
3.9.
Internet Data Mining Using Statistical Techniques
/
Kuldeep Kumar
.......................1446
Chapter
3.10.
Mining E-Mail Data
/ Steffen
Bickel and Tobias Scheffer
........................................1454
Chapter
3.11.
Exploiting Captions for Web Data Mining I Neil C. Rowe
......................................1461
Chapter
3.12.
Agent-Mediated Knowledge Acquisition for User Profiling
/
A. Andreevskaia, R. Abi-Aad, and T. Radhakrishnan
.....................................................................1486
Chapter
3.13.
Mobile User Data Mining and its Applications I John
Goh
and David
Tatuar
............................................................................................................................1502
Chapter
3.14.
Mobile Phone Customer Type Discrimination via Stochastic Gradient
Boosting
/
Dan Steinberg, Mikhaylo Golovnya, and Nicholas Scott Cardell
.................................1519
Chapter
3.15.
Intelligent Cache Management for Mobile Data Warehouse Systems
/
Shi-Ming Huang, Binshan Lin, and Qun-Shi Deng
........................................................................1539
Chapter
3.16.
VRMiner: A Tool for Multimedia Database Mining with Virtual Reality
/
H. Azzag, F. Picarougne, C. Guinoi, andG. Venturini
...................................................................1557
Chapter
3.17.
Spatial Navigation Assistance System for Large Virtual
Environments: The Data Mining Approach
/
Mehmed Kantardzic, Pedram Sadeghian,
and Walaa M. Sheta
........................................................................................................................1573
Chapter
3.18.
Bitmap Indices for Data Warehouses
/
Kurt Stockinger
andKeshengWu
..............................................................................................................................1590
Chapter
3.19.
Indexing in Data Warehouses: Bitmaps and Beyond
/
Karen C. Davis andAshimu Gupta
.................................................................................................1606
Chapter
3.20.
Visualization Techniques for Data Mining
/
Herna
L.
Viktor
and
Eric Paquet
..............................................................................................................................1623
Chapter
3.21.
Video Data Mining
/
JungHwan Oh, JeongKyu Lee,
and Sue Hwang
.............................................................................................................................. 631
Chapter
3.22.
Interactive Visual Data Mining IShouhong WangandHai Wang
...........................1638
Chapter
3.23.
Data Mining in Gene Expression Analysis: A Survey
/
Min
Han,
Le
Gruenwald, and Tyrrell Conway
...............................................................................1643
Chapter
3.24.
A Haplotype Analysis System for Genes Discovery of
Common Diseases/
Takas
hiKido
..................................................................................................1674
Section
4.
Utilization and Application
This section
introduces
and discusses a variety of the existing applications of data mining and warehous¬
ing that have influenced government, culture, and biology and also proposes new ways in which data
mining and warehousing can be implemented in society. Within these selections, particular issues, such
as the use of data mining and warehousing in human resources and the incorporation of data analysis
techniques into homeland security strategies, are explored and debated. Contributions included in
this section provide excellent coverage of today s IT community and insight into how data mining and
warehousing impacts the social fabric of our present-day global village.
Chapter
4.1.
Strategic Utilization of Data Mining
/ Chandras. Amaravadi..................................1689
Chapter
4.2.
Biological Data Mining
/
George Tzanis,
Christos Berberidis,
and
Ioannis Vlahavas
.............................................................................................................................1696
Chapter
4.3.
Biomedical
Data Mining Using RBF Neural Networks
/
Feng
Chu
and
Lipo
Wang
...............................................................................................................1706
Chapter
4.4.
Bioinformatics Data Management and Data Mining
/
Boris
Galitsky
.......................1714
Chapter
4.5.
Deterministic Motif Mining in Protein Databases
/
Pedro
Gabriel Ferreira
and Paulo Jorge
Azevedo
................................................................................................................1722
Chapter
4.6.
Differential Association Rules: Understanding Annotations in Protein
Interaction Networks
/
Christopher
Bese
mann,
Anne
Dentón, Ajay
Yekkirala,
Ron Hutchison, and Marc Anderson
...............................................................................................
I747
Chapter
4.7.
Data Mining and Knowledge Discovery in Metabolomics
/
Christian Baumgartner and
Armin Graber....................................................................................] 759
Chapter
4.8.
Comparative Genome Annotation Systems
/
Kwangmin Choi and
Sun Kim
...........................................................................................................................................
J734
Chapter
4.9.
The Application of Data Mining Techniques in Health Plan Population
Management: A Disease Management Approach
/
Theodore L. Perry, Travis Tucker,
Laurel R. Hudson, William Gandy, Amy L. Neftzger, and Guy B.
Hamar
......................................1799
Chapter
4.10.
Data Mining Medical Digital Libraries
/
Colleen Cunningham
and Xiaohua
Ни
..............................................................................................................................1810
Volume IV
Chapter
4.11.
Data Mining in Diabetes Diagnosis and Detection
/
Indranil
Bose
.........................1817
Chapter
4.12.
Data Warehousing and Analytics in Banking: Concepts
/
L. Venkat Narayanan
.......................................................................................................................1825
Chapter
4.13.
Data Warehousing and Analytics in Banking: Implementation
/
L. Venkat Narayanan
.......................................................................................................................1840
Chapter
4.14.
Beyond Classification: Challenges of Data Mining for
Credit Scoring I Anna
Olecka
.........................................................................................................1855
Chapter
4.15.
ATOPSIS Data Mining Demonstration and Application to
Credit Scoring
/
Desheng Wu and David
L
Olson
.........................................................................1877
Chapter
4.16.
The Utilization of Business Intelligence and Data Mining in the
Insurance Marketplace
/
Jeff Hoffman
............................................................................................1888
Chapter
4.17.
Ontology-Based Data Warehousing and Mining Approaches in
Petroleum Industries
/
Shastri
L
Nimmagadda and Heinz
Dreher................................................1901
Chapter
4.18.
A Study on Web Searching: Overlap and Distance of the Search
Engine Results
/
ShanfengZhu, Xiaotie Deng, Qizhi Fang, and Weimin Zheng
............................1926
Chapter
4.19.
Data Mining in Web Services Discovery and Monitoring
/
RichiNayak
.....................................................................................................................................1938
Chapter
4.20.
A Data Mining Driven Approach for Web Classification and Filtering
Based on
Multimodal
Content Analysis
/
Mohamed
Hammami, YoussefChahir,
and
Liming Chen
....................................................................................................................................1958
Chapter
4.21.
Acquiring Semantic Sibling Associations from Web Documents
/
Marko
Brunzel and
Myra
Spiliopoulou
..........................................................................................1987
Chapter
4.22.
Traversal Pattern Mining in Web Usage Data
/
Yongqiao Xiao and
Jenq-Foung (J.F.) Yao
.....................................................................................................................2004
Chapter
4.23.
Facilitating and Improving the Use of Web Services with Data Mining
/
RichiNayak
.....................................................................................................................................2022
Chapter
4.24.
E-Mail Worm Detection Using Data Mining
/
Mohammad M. Masud,
Latifur Khan, and Bhavani Thuraisingham
....................................................................................2036
Chapter
4.25.
User-Centered Interactive Data Mining
/
Yan Zhao, Yaohua Chetu
andYiyuYao
....................................................................................................................................2051
Chapter
4.26.
Advanced Data Mining and Visualization Techniques with Probabilistic
Principal Surfaces: Applications to Astronomy and Genetics
/
Antonino Statano,
Lara
De Vinco,
Giuseppe
Longo,
and Roberto
Tagliaferri.............................................................
2067
Chapter
4.27.
Using Data Mining for Forecasting Data Management Needs
/
Qingyu Zhang and
Richard S. Segali
..............................................................................................2088
Chapter
4.28.
Visual Data Mining for Discovering Association Rules
/
Kesaraporn Techapichetvanich and Amitava Datta
.......................................................................2105
Chapter
4.29.
Generalization Data Mining in Fuzzy Object-Oriented Databases
/
Rafal
Angryk, Roy Ladner, and Frederick E.
Petry
........................................................................2121
Chapter
4.30.
Fuzzy Miner: Extracting Fuzzy Rules from Numerical Patterns
/
Nikos Pelekis, Babis Theodoulidis, loannis Kopanakis, and Yannis Theodoridis
..........................2141
Chapter
4.31.
Empowering the
OLAP
Technology to Support Complex
Dimension Hierarchies
I Svetlana
Mansmann and Marc H.
Scholl...............................................2164
Chapter
4.32.
Understanding Decision-Making in Data Warehousing and Related
Decision Support Systems: An Explanatory Study of a Customer Relationship
Management Application
/
John D. Wells and
Traci
J. Hess
..........................................................2185
Chapter
4.33.
Statistical Sampling to Instantiate Materialized View Selection
Problems in Data Warehouses
/
Mesbah U. Ahmed,
Vikas Agrawal,
Udayan Nandkeolyar,
and P. S. Sundararaghavan
.............................................................................................................2201
Chapter
4.34.
Development of Control Signatures with a Hybrid Data Mining and
Genetic Algorithm
/
Alex Burns, Shital Shah, and Andrew Kusiak
................................................2226
Chapter
4.35.
Feature Selection for the Promoter Recognition and Prediction
Problem
/
George Potamias and Alexandras Kanterakis
...............................................................2248
Chapter
4.36.
Data Warehousing Search Engine
/
Hadrian Peter and
Charles Greenidge
..........................................................................................................................2263
Section
5.
Organizational and Social Implications
This section includes a wide range of research pertaining to the social and organizational impact of data
mining and warehousing around the world. Chapters introducing this section illustrate varying perspec¬
tives on organizational data mining, as well as its relationship to cognition. Other contributions discuss
the potential of data mining and warehousing for transforming business, government and medicine, as
well as providing insight into individual behavior. Particular selections explain the design of a data
model for social applications, provide insight into the implications of data mining and warehousing in
the banking sector, and explain data mining s use in generating credit scores. The inquiries and methods
presented in this section offer insight into the integration of data mining and warehousing in social and
organizational settings while also emphasizing the potential for future societal applications.
Chapter
5.1.
Data Mining in Practice
/
Sherry Y. Chen andXiaohui Liu
.......................................2273
Chapter
5.2.
Model Indentification through Data Mining
/
Diego
Liberati
....................................2281
Chapter
5.3.
Organizational Data Mining
(ODM):
An Introduction
/
Hamid
R.
Nemati
and Christopher D.
Barko
..................................................................................2289
Chapter
5.4.
Constructionist Perspective of Organizational Data Mining/
Isabel Ramos and
João Álvaro Carvalho
.......................................................................................2296
Chapter
5.5.
The Role of Data Mining in Organizational Cognition
/
Chandra S. Amaravadi and FarhadDaneshgar
.............................................................................2302
Chapter
5.6.
Ontology-Based Interpretation and Validation of Mined Knowledge:
Normative and Cognitive Factors in Data Mining I Ana Isabel
Canhoto
.......................................2316
Chapter
5.7.
Design of a Data Model for Social Network Applications
/
Susanta
Mitra,
Aditya Bagchi, andA.K.Bandyopadhyay
...............................................................2338
Chapter
5.8.
Humanitites Data Warehousing/ Janet Delve
............................................................2364
Chapter
5.9.
Data Mining in Human Resources
/
Marvin D. Troutt and
Lori K. Long
....................................................................................................................................2371
Chapter
5.10.
Privacy Preserving Data Mining, Concepts, Techniques, and
Evaluation Methodologies
/
Igor
Nai Fovino
.................................................................................2379
Chapter
5.11.
Privacy-Preserving Data Mining and the Need for Confluence of
Research and Practice
/
Lixin
Fu,
Hamid
Nemati,
and Fereidoon
Sadri........................................
2402
Chapter
5.12.
Data Mining in the Federal Government
/
Les Pang
...............................................2421
Volume
V
Chapter
5.13.
Data Warehousing and the Organization of Governmental Databases
/
Franklin Maxwell Harper
...............................................................................................................2427
Chapter
5.14.
Data Mining and the Banking Sector: Managing Risk in Lending
and Credit Card Activities I
Àkos Felsõvályi
and Jennifer
Courant
...............................................2438
Chapter
5.15.
Data Mining for Credit Scoring
/
lndranil
Bose,
Cheng
Pni
Katt,
Chi King
Tsz,
Lau
Wai Ki, and Wong Cho Hung
............................................................................2449
Chapter
5.16.
Credit Card Users Data Mining
/
André de Carvalho,
Antonio P.
Braga,
and Teresa Ludermir
.........................................................................................2464
Chapter
5.17.
Data Mining for Supply Chain Management in Complex Networks
/
Mahesh S. Raisinghani and Manoj
К
Singh
..................................................................................2468
Chapter 5.18.TSeural Network-Based Stock Market Return Forecasting Using Data
Mining for Variable Reduction
/
DavidEnke
..................................................................................2476
Chapter
5.19.
Data Mining and Knowledge Discovery in Healthcare Organizations:
A Decision-Tree Approach
/
Murat
Caner Testik, George C. Runger,
Bradford Kirkman-Liff, and Edward A. Smith
................................................................................2494
Chapter
5.20.
Data Mining Techniques and Medical Decision Making for
Urological Dysfunction
/ N.
Sriraam, V. Natasha, and
H
Kam....................................................2506
Chapter
5.21.
Heuristics in Medical Data Mining
/
Susan E. George
............................................2517
Chapter
5.22.
An Approach to Mining Crime Patterns
/
SikhaBagui
............................................2523
Chapter
5.23.
Web Usage Mining Data Preparation
/
BamshadMobasher
....................................2551
Chapter
5.24.
Classification Of 3G Mobile Phone Customers
/
Ankur Jain,
Lalit
Wangikar, Martin Ahrens, Ranjan Rao, Suddha Sattwa Kundu, and
Sutirtha Ghosh
................................................................................................................................2558
Chapter
5.25.
Impediments to Exploratory Data Mining Success
/
JeffZeanah
............................2566
Section
6.
Managerial Impact
This section presents contemporary coverage of the more formal implications of data mining and ware¬
housing, more specifically related to the corporate and managerial utilization of information-sharing
technologies and applications, and how these technologies can he facilitated within organizations. Core
ideas such as successful data mining in franchise organizations and the use of data analysis to predict
customer behavior are discussed throughout these chapters. Contributions within this section seek to
answer the fundamental question of data mining and warehousing implementation in organizations:
How can particular techniques best be integrated into businesses and what are the potential obstacles
to such integration? Particular chapters provide case studies of data mining and warehousing use in
business and address some of the most significant issues that have arisen from data mining and ware¬
housing implementation.
Chapter
6.1.
Data Mining and Business Intelligence: Tools, Technologies,
and Applications I Jeffrey Hsu
........................................................................................................2584
Chapter
6.2.
Data Mining and Decision Support for Business and Science
/
Auroop R. Ganguly,
Amar
Gupta, and Shiraj Khan
.......................................................................2618
Chapter
6.3.
Data Warehousing Interoperability for the Extended Enterprise
/
Aristides Triantafillakis, Panagiotis Kanellis, and Drakoulis
Martakos
........................................2626
Chapter
6.4.
Data Warehousing and Mining in Supply Chains
/
Richard
Mathieu
and
Reuven
R. Levary
.....................................................................................................................2637
Chapter
6.5.
Management of Data Streams for Large-Scale Data Mining
/
Jon R. Wright, Gregg T. Vesonder, and Tamraparni Dasu
.............................................................2644
Chapter
6.6.
Customized Recommendation Mechanism Based on Web Data Mining
and Case-Based Reasoning I Jin Sung Kim
....................................................................................2659
Chapter
6.7.
Gaining Strategic Advantage through Bibliomining: Data Mining for
Management Decisions in Corporate, Special, Digital, and Traditional Libraries
/
Scott Nicholson and Jeffrey Stanton
...............................................................................................2673
Chapter
6.8.
Expanding Data Mining Power with System Dynamics
/
Edilberto
Casado
............................................................................................................................2688
Chapter
6.9.
Data Mining and Mobile Business Data
/
Richi Nayak
..............................................2697
Chapter
6.10.
Neural Data Mining System for Trust-Based Evaluation in Smart
Organizations
/
T. T.
Wong
..............................................................................................................2704
Chapter
6.11.
Data Mining in Franchise Organizations
/
Ye-Sho Chen, Robert Just is,
and P. PeteChong
...........................................................................................................................2722
Chapter
6.12.
Translating Advances in Data Mining in Business Operations:
The Art of Data Mining in Retailing
/
Henry Dillon and Beverley Hope
.......................................2734
Chapter
6.13.
Data Warehousing: The 3M Experience
/
Hugh J. Watson,
Barbara H. Wixom, and Dale
L
Goodhue
......................................................................................2749
Chapter
6.14.
Business Data Warehouse: The Case of Wal-Mart
/
Indranil
Bose,
Lam Albert
Kar
Chun, Leung Vivien Wai Yue, Li Hoi Wan
Ines,
and Wong Oi Ling Helen
.................................................................................................................2762
Chapter
6.15.
A Porter Framework for Understanding the Strategic Potential of
Data Mining for the Australian Banking Industry
/
Kate A. Smith and Mark S. Dale
....................2772
Chapter
6.16.
Data Mining for Combining Forecasts in Inventory Management
/
Chi Kin Chan
..................................................................................................................................2792
Chapter
6.17.
Analytical Customer Requirement Analysis Based on Data Mining
/
Jianxin (Roger) Jiao, Yiyang Zhang, and Martin
Helander
...........................................................2798
Chapter
6.18.
Predicting Future Customers via Ensembling Gradually Expanded Trees
/
Yang Yu, Chuan Zhan, Xu-Ying Liu, Ming Li, and Zhi-Hua Zhou
..................................................2816
Chapter
6.19.
Marketing Data Mining
/
Victor S.
Y. Lo
...................................................................2824
Section
7.
Critical Issues
This section addresses conceptual and theoretical issues related to the field of data mining and ware¬
housing, yvhich include the ethical implications of data collection and the numerous approaches adopted
by researchers that aid in making data mining and warehousing
morę
effective. Within these chapters,
the reader is presented with an in-depth analysis of the most current and relevant conceptual inquires
within this growing field of study. Particular chapters address data partitioning, data warehouse re¬
freshment, and mining with incomplete data sets. Overall, contributions within this section ask unique,
often theoretical questions related to the study of data mining and warehousing and, more often than
not. conclude that solutions are both numerous and contradictory.
Chapter
7.1.
Ethics Of Data Mining ¡Jack Cook
...........................................................................2834
Chapter
7.2.
Ethical Dilemmas in Data Mining and Warehousing/ Joseph
A. Cazier
and Ryan
С
LaBrie
.........................................................................................................................2841
Chapter
7.3.
Privacy and Confidentiality Issues in Data Mining
/ Yücel
Saygin
............................2850
Chapter
7.4.
Privacy Implications of Organizational Data Mining
/ Hamid R
Nemati,
Charmion Brathwaite, and Kara Harrington
.................................................................................2856
Chapter
7.5.
Privacy in Data Mining Textbooks
/
James Lawler and
John C. Molluzzo
............................................................................................................................2872
Chapter
7.6.
Data Mining for intrusion Detection
/
Alebandar Lazarevic
....................................2880
Chapter
7.7.
E-Commerce and Data Mining: Integration Issues and Challenges
/
Parviz Partow-Navid and
Ludwig
Slusky
.......................................................................................2888
Chapter
7.8.
A Data Mining Approach to Formulating a Successful Purchasing
Negotiation Strategy
/
Hokey
Min
and Ahmed Emam
....................................................................2900
Chapter
7.9.
Data Mining Medical Information: Should Artificial Neural Networks
Be Used to Analyse Trauma Audit Data?
/
Thomas Chesney, Kay Penny, Peter Oakley,
Simon Davies, David Chesney, Nicola Majfulli, and John
Templeton
...........................................2915
Chapter
7.10.
A Data Mining Approach to Diagnosing Student Learning Problems in
Sciences Courses
/
Gwo-Jen Hwang
...............................................................................................2928
Chapter
7.11.
Effective Intelligent Data Mining Using Dempster-Shafer Theory
/
Malcolm J. Beynon
..........................................................................................................................2943
Chapter
7.12.
An Intelligent Support System Integrating Data Mining and Online
Analytical Processing
/
Rahul Singh, Richard T. Redmond, and Victoria Yoon
.............................2964
Chapter
7.13.
A Successive Decision Tree Approach to Mining Remotely Sensed
Image Data
/
Jianting Zhang, Wieguo Liu, and
Le Gruenwald
......................................................2978
Chapter
7.14.
Mining for Mutually Exclusive Items in Transaction Databases
/
George Tzanis and
Christos Berberidis..........................................................................................2993
Chapter
7.15.
Re-Sampling Based Data Mining Using Rough Set Theory
/
Benjamin Griffiths and Malcolm J. Beynon
....................................................................................3005
Chapter
7.16.
Data Mining with Incomplete Data
/
Hai
Wang and Shouhong Wang
.....................3027
Chapter
7.17.
Routing Attribute Data Mining Based on Rough Set Theory
/
YanbingLiu, ShixinSun, Menghao Wang, and Hong Tang
............................................................3033
Volume VI
Chapter
7.18.
Data Warehouse Refreshment
/
Alkis Simitisis,
Panos Vassiliadis,
Spiros Skiadopoulos, and
Timos Sellis
............................................................................................3049
Chapter
7.19.
An Algebraic Approach to Data Quality Metrics for Entity
Resolution Over Large
Datasets
/
John Talburt, Richard Wang, Kimberly Hess,
and Emily Km
.................................................................................................................................
3067
Chapter
7.20.
A Hybrid Approach for Data Warehouse View Selection
/
Biren Shah, Karthik Ramachandran, and Vijay Raghavan
.............................................................3085
Chapter
7.21.
A Space-Efficient Protocol for Consistency of External View
Maintenance on Data Warehouse Systems: A Proxy Approach
/
Shi-Ming Huang,
DavidC. Yen, andHsiang-Yuan Hsueh
...........................................................................................3116
Chapter
7.22.
DWFIST: The Data Warehouse of Frequent Itemsets Tactics
Approach
/
Rodrigo
Salvador
Monteiro, Geraldo Zimbrão,
Holger Schwarz,
Bernhard Mitschang, and
Jano
Moreira de Souza
...........................................■..............................3142
Chapter
7.23.
A Hyper-Heuristic for Descriptive Rule Induction
/
Tho Hoan Pham
and
Tu Bao
Ho
................................................................................................................................
3
164
Chapter
7.24.
Improved Data Partitioning for Building Large ROLAP Data
Cubes in Parallel
/
Ying Chen, Frank
Dehne,
Toad Eavis, and A. Rau-Chaplin
.............................3176
Chapter
7.25.
An Ontology of Data Modelling Languages: A Study Using a
Common-Sense Realistic Ontology I Simon K. Milton and Ed Kazmierczak
................................3194
Chapter
7.26.
Robust Classification Based on Correlations Between Attributes
/
Alexandros Nanopoulos,
Apostólos
N.
Papadopoulos, Yannis Manolopoulos, and
Tatjana Welzer-Druzovec................................................................................................................3212
Chapter
7.27.
Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse
/
Yun Sing Koh, Nathan Rountree, and Richard O Keefe
..................................................................3222
Chapter
7.28.
Discovering Surprising Instances of Simpson s Paradox in
Hierarchical Multidimensional Data
/
Carem
С.
Fabris
and Alex
A. Freitas
.................................3235
Chapter
7.29.
Discovering Frequent Embedded Subtree Patterns from
Large Databases of Unordered Labeled Trees
/
Yongqiao Xiao, Jenq-Foung Yao,
and Guizhen Yang
...........................................................................................................................3252
Chapter
7.30.
A Single Pass Algorithm for Discovering Significant Intervals in
Time-Series Data
/
Sugar
Savia
and Sharma Chakravarthy
..........................................................3272
Chapter
7.31.
SeqPAM: A Sequence Clustering Algorithm for Web
Personalization
/
Pradeep Kumar,
Raju S.
Варі,
and P. Radha Krishna
........................................3285
Chapter
7.32.
Kernal Width Selection for SVM Classification: A Meta-Learning
Approach
/
Shawkat AH and Kate A. Smith
........................................................................ 3308
Chapter
7.33.
A Parallel Implementation Scheme of Relational Tables Based on
Multidimensional Extendible Array
/
K. M. Azharul Hasan, Tatsuo Tsuji, and
Ken Higuchi
........................................................................................................ 3324
Section
8.
Emerging Trends
This section highlights research potential within the field of data mining and warehousing while also
exploring uncharted areas of study for the advancement of the discipline. Introducing this section are
selections providing
.
Discussions exploring semantic data mining. Web data warehousing and spatio-
temporal databases provide insight into forthcoming issues in data mining and warehousing study.
These contributions, which conclude this exhaustive, multi-volume set, provide emerging trends and
suggestions for future research within this rapidly expanding discipline.
Chapter
8.1.
Toward Integrating Data Warehousing with Data Mining Techniques
/
Rokia
Missaoui, Ganaël Jatteau,
Ameur Boujenoui, and
Sami Nabouali
......................................3346
Chapter
8.2.
Combining Data Warehousing and Data Mining Techniques for Web
Log Analysis
/ Torben
Bach Pedersen,
Jesper
Thorhauge, and
Seren
E.
Jespersen
......................3364
Chapter
8.3.
Web Data Warehousing Convergence: From Schematic to Systematic
/
D. Xuun Le,J. Wenny Rahayu, and David
Tatuar
..........................................................................3386
Chapter
8.4.
Web Technology and Data Warehouse Synergies
/
John M. Artz
................................3411
Chapter
8.5.
Metadata Management: A Requirement for Web Warehousing and
KnowledgeManagement
/
Gilbert W. ¿aware
.................................................................................3416
Chapter
8.6.
An Immune Systems Approach for Classifying Mobile Phone Usage
/
Hanny
Yulius Limanto,
Тау
Joe Cing, and Andrew Watkins
...........................................................3440
Chapter
8.7.
User Interface Formalization in Visual Data Mining
/
Tiziana
Catarei,
Stephen Kimani, and
Stefano Lodi..................................................................................................
3451
Chapter
8.8.
Mining in
Spatio-Temporal
Databases
/
Junmei Wang, Wynne Hsu,
and Mong Li Lee
.............................................................................................................................3477
Chapter
8.9.
Algebraic Reconstruction Technique in Image Reconstruction Based
on Data Mining
/
Zhong Qu
............................................................................................................3493
Chapter
8.10.
Evolutionary Induction of Mixed Decision Trees
/
Marek
Kretowski
and
Marek
Grzes
.............................................................................................................................3509
Chapter
8.11.
Semantic Data Mining
/
Protima Banerjee, Xiaohua
Ни,
andlllhoiYoo
..................................................................................................................................3524
Chapter
8.12.
Metadata- and Ontology-Based Semantic Web Mining/
Marie
Aude
Auf
aure,
Bénédicte Le Grand, Michel
Soto, and Nacerá
Bennacer
...........................3531
Chapter
8.13.
Integrating Semantic Knowledge with Web Usage Mining for
Personalization
/
Honghua Dai and Batnshad Mobasher
...............................................................3557
Chapter
8.14.
Mining in Music Databases
/
Ioannis Karydis,
Alexandras
Nanopoulos,
and Yannis Manolopoulos
...............................................................................................................3586
Chapter
8.15.
Multimedia Data Mining Concept/
Janusz
Swierzowicz
..........................................3611
Chapter
8.16.
Robust Face Recognition for Data Mining
/
Brian
C. Loveli
andShaokang Chen
........................................................................................................................3621
Chapter
8.17.
Data Mining and Homeland Security
/
Jeffrey W.
Seifert.........................................3630
Chapter
8.18.
Homeland Security Data Mining and Link Analysis
/
Bhavani Thuraisingham
..................................................................................................................3639
Chapter
8.19.
Seismological Data Warehousing and Mining: A Survey/
Gerasimos
Marketos,
Yannis Theodoridis, Ioannis S.
Kalogeras...................................................3645
Chapter
8.20.
Realizing Knowledge Assets in the Medical Sciences with Data Mining:
An Overview I Adam Fadlalla and Nilmini Wickramasinghe
........................................................3662
Chapter
8.21·.
Mining Clinical Trial Data
/
Jose Ma. J. Alvir, Javier Cabrera,
Frank Caridi, and Ha Nguyen
........................................................................................................3675
Chapter
8.22.
Vertical Database Design for Scalable Data Mining
/
William Perrizo,
Qiang Ding,
Манит
Serazi, Taufik Abidin, and Baoying Wang
.....................................................3694
DATA WAREHOUSING
AND MINING
Concepts.
Methodologies, Tools, and implications
In recent years, the science of managing and analyzing targe
datasets
has
emerged as a critical area of research, in the race to answer vita! questions
and make knowledgeable decisions, impressive amounts of data are now
being generated at a rapid pace, increasing the opportunities and challenges
associated with the
abiîity
to effectively analyze this data,
5
Warehousing and Mining: Concepts, Methodologies, Tools, and
Applications provides the most comprehensive
compilation
of research avail¬
able in this emerging and increasingly important
fieîd.This
six-volume set offers
tools, designs, and outcomes of the utilization of data mining and warehousing
technologies, such as algorithms, concept lattices.,
multìdimensìona} data,
and
online analytical processing. With more than
230
chapters contributed by over
500
experts from around the globe, this authoritative collection will provide
libraries with the essential reference on data mining and wareftousing.
Topics Covered
■
Active databases
■
fernel methods
■
Algorithms
■
Knowledge model
«
Automation
■
togic constraints
■
Benchmarking
■
Mteraarray technologies
■
Clustering
* ■
Multidimensional databases
m
Concept lattices
■
Neural
networks
■
Conceptual modeling
■
Object orientation
■
Control flow modeling
■
Object-relational databases
■
Database maintenance
■
ODBMS
•
Database systems
·
Online analytical processing
(OLAP)
«
Data consultants
■
Pattern discovery
•
Data flow analysis
»
Performance evaluation
■
Data manipulation
■
Regression testing
■
Data modeling
■
Relational database implementation
•
Data warehouses
■
Software maintenance
•
Dynamic access patterns
■
Software testing
Ш
Firewall
ш
Structured query language (SQL)
■
Formal methods
■
Supply chain management
•
Fuzzy applications
·
■
Impact analysis
· ■
•
Information
systerns
■
Integration
■
Integrity constraints
|
| any_adam_object | 1 |
| author_GND | (DE-588)132281031 |
| building | Verbundindex |
| bvnumber | BV023211008 |
| ctrlnum | (OCoLC)635127953 (DE-599)BVBBV023211008 |
| format | Book |
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| id | DE-604.BV023211008 |
| illustrated | Illustrated |
| indexdate | 2024-12-23T20:57:55Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016397069 |
| oclc_num | 635127953 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-384 |
| owner_facet | DE-355 DE-BY-UBR DE-384 |
| physical | LXXI, 590, 20 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Information Science Reference |
| record_format | marc |
| series2 | Premier reference source |
| spellingShingle | Data warehousing and mining concepts, methodologies, tools, and applications |
| title | Data warehousing and mining concepts, methodologies, tools, and applications |
| title_auth | Data warehousing and mining concepts, methodologies, tools, and applications |
| title_exact_search | Data warehousing and mining concepts, methodologies, tools, and applications |
| title_full | Data warehousing and mining concepts, methodologies, tools, and applications 1 John Wang [ed.] |
| title_fullStr | Data warehousing and mining concepts, methodologies, tools, and applications 1 John Wang [ed.] |
| title_full_unstemmed | Data warehousing and mining concepts, methodologies, tools, and applications 1 John Wang [ed.] |
| title_short | Data warehousing and mining |
| title_sort | data warehousing and mining concepts methodologies tools and applications |
| title_sub | concepts, methodologies, tools, and applications |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016397069&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016397069&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023211000 |
| work_keys_str_mv | AT wangjohn datawarehousingandminingconceptsmethodologiestoolsandapplications1 |