Applied data mining

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Hauptverfasser: Xu, Guandong (VerfasserIn), Zong, Yu (VerfasserIn), Yang, Zhenglu (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Boca Raton [u.a.] CRC Press 2013
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Datensatz im Suchindex

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adam_text Contents Preface v Part I: Fundamentals 1. Introduction 3 1.1 Background 3 1.1.1 Data Mining — Definitions and Concepts 4 1.1.2 Data Mining Process 6 1.1.3 Data Mining Algorithms 10 1.2 Organization of the Book 16 1.2.1 Part 1: Fundamentals 17 1.2.2 Part 2: Advanced Data Mining 18 1.2.3 Part 3: Emerging Applications 19 1.3 The Audience of the Book 19 2. Mathematical Foundations 21 2.1 Organization of Data 21 2.1.1 Boolean Model 22 2.1.2 Vector Space Model 22 2.1.3 Graph Model 23 2.1.4 Other Data Structures 26 2.2 Data Distribution 27 2.2.1 Univariate Distribution 27 2.2.2 Multivariate Distribution 28 2.3 Distance Measures 29 2.3.1 Jaccard distance 30 2.3.2 Euclidean Distance 30 2.3.3 Minkowski Distance 31 2.3.4 Chebyshev Distance 32 2.3.5 Mahalanobis Distance 32 2.4 Similarity Measures 33 2.4.1 Cosine Similarity 33 2.4.2 Adjusted Cosine Similarity 34 viii Applied Data Mining 2.4.3 Kullback-Leibler Divergence 35 2.4.4 Model-based Measures 37 2.5 Dimensionality Reduction 38 2.5.1 Principal Component Analysis 38 2.5.2 Independent Component Analysis 40 2.5.3 Non-negative Matrix Factorization 41 2.5.4 Singular Value Decomposition 42 2.6 Chapter Summary 43 3. Data Preparation 45 3.1 Attribute Selection 46 3.1.1 Feature Selection 46 3.1.2 Discretizing Numeric Attributes 49 3.2 Data Cleaning and Integrity 50 3.2.1 Missing Values 50 3.2.2 Detecting Anomalies 51 3.2.3 Applications 52 3.3 Multiple Model Integration 53 3.3.1 Data Federation 53 3.3.2 Bagging and Boosting 54 3.4 Chapter Summary 55 4. Clustering Analysis 57 4.1 Clustering Analysis 57 4.2 Types of Data in Clustering Analysis 59 4.2.1 Data Matrix 59 4.2.2 The Proximity Matrix 61 4.3 Traditional Clustering Algorithms 63 4.3.1 Partitional methods 63 4.3.2 Hierarchical Methods 68 4.3.3 Density-based methods 74 4.3.4 Grid-based Methods 77 4.3.5 Model-based Methods 80 4.4 High-dimensional clustering algorithm 83 4.4.1 Bottom-up Approaches 84 4.4.2 Top-down Approaches 86 4.4.3 Other Methods 88 4.5 Constraint-based Clustering Algorithm 89 4.5.1 COP K-means 90 4.5.2 MPCK-means 90 4.5.3 AFCC 91 4.6 Consensus Clustering Algorithm 92 4.6.1 Consensus Clustering Framework 93 4.6.2 Some Consensus Clustering Methods 95 4.7 Chapter Summary 96 Contents ix 5. Classification 100 5.1 Classification Definition and Related Issues 101 5.2 Decision Tree and Classification 103 5.2.1 Decision Tree 103 5.2.2 Decision Tree Classification 105 5.2.3 Hunt s Algorithm 106 5.3 Bayesian Network and Classification 107 5.3.1 Bayesian Network 107 5.3.2 Backpropagation and Classification 109 5.3.3 Association-based Classification 110 5.3.4 Support Vector Machines and Classification 112 5.4 Chapter Summary 115 6. Frequent Pattern Mining 117 6.1 Association Rule Mining 117 6.1.1 Association Rule Mining Problem 118 6.1.2 Basic Algorithms for Association Rule Mining 120 6.2 Sequential Pattern Mining 124 6.2.1 Sequential Pattern Mining Problem 125 6.2.2 Existing Sequential Pattern Mining Algorithms 126 6.3 Frequent Subtree Mining 137 6.3.1 Frequent Subtree Mining Problem 137 6.3.2 Data Structures for Storing Trees 138 6.3.3 Maximal and closed frequent subtrees 141 6.4 Frequent Subgraph Mining 142 6.4.1 Problem Definition 142 6.4.2 Graph Representation 143 6.4.3 Candidate Generation 144 6.4.4 Frequent Subgraph Mining Algorithms 145 6.5 Chapter Summary 146 Part II: Advanced Data Mining 7. Advanced Clustering Analysis 153 7.1 Introduction 153 7.2 Space Smoothing Search Methods in Heuristic Clustering 155 7.2.1 Smoothing Search Space and Smoothing Operator 156 7.2.2 Clustering Algorithm based on Smoothed Search Space 161 7.3 Using Approximate Backbone for Initializations in Clustering 163 7.3.1 Definitions and Background of Approximate Backbone 164 7.3.2 Heuristic Clustering Algorithm based on 167 Approximate Backbone 7.4 Improving Clustering Quality in High Dimensional Space 169 7.4.1 Overview of High Dimensional Clustering 169 x Applied Data Mining 7.4.2 Motivation of our Method 171 7.4.3 Significant Local Dense Area 171 7.4.4 Projective Clustering based on SLDAs 175 7.5 Chapter Summary 178 8. Multi-Label Classification 181 8.1 Introduction 181 8.2 What is Multi-label Classification 182 8.3 Problem Transformation 184 8.3.1 Binary Relevance and Label Powerset 185 8.3.2 Classifier Chains and Probabilistic Classifier Chains 187 8.3.3 Decompose the Label Set 189 8.3.4 Transform Original Label Space to Another Space 191 8.4 Algorithm Adaptation 192 8.4.1 KNN-based methods 192 8.4.2 Learn the Label Dependencies by the Statistical Models 194 8.5 Evaluation Metrics and Datasets 195 8.5.1 Evaluation Metrics 195 8.5.2 Benchmark Datasets and the Statistics 199 8.6 Chapter Summary 200 9. Privacy Preserving in Data Mining 204 9.1 The K-Anonymity Method 204 9.2 The 1-Diversity Method 208 9.3 The ŕ-Closeness Method 210 9.4 Discussion and Challenges 211 9.5 Chapter Summary 211 Part III: Emerging Applications 10. Data Stream 215 10.1 General Data Stream Models 215 10.2 Sampling Approach 216 10.2.1 Random Sampling 218 10.2.2 Cluster Sampling 219 10.3 Wavelet Method 220 10.4 Sketch Method 222 10.4.1 Sliding Window-based Sketch 223 10.4.2 Count Sketch 224 10.4.3 Fast Count Sketch 225 10.4.4 Count Min Sketch 225 10.4.5 Some Related Issues on Sketches 226 10.4.6 Applications of Sketches 227 10.4.7 Advantages and Limitations of Sketch Strategies 227 Contents xi 10.5 Histogram Method 228 10.5.1 Dynamic Construction of Histograms 230 10.6 Discussion 231 10.7 Chapter Summary 232 11. Recommendation Systems 236 11.1 Collaborative Filtering 236 11.1.1 Memory-based Collaborative Recommendation 237 11.1.2 Model-based Recommendation 238 11.2 PLSA Method 238 11.2.1 User Pattern Extraction and Latent Factor 240 Recognition 11.3 Tensor Model 242 11.4 Discussion and Challenges 244 11.4.1 Security and Privacy Issues 244 11.4.2 Effectiveness Issue 245 11.5 Chapter Summary 246 12. Social Tagging Systems 248 12.1 Data Mining and Information Retrieval 248 12.2 Recommender Systems 250 12.2.1 Recommendation Algorithms 251 12.2.2 Tag-Based Recommender Systems 254 12.3 Clustering Algorithms in Recommendation 257 12.3.1 K-means Algorithm 257 12.3.2 Hierarchical Clustering 259 12.3.3 Spectral Clustering 260 12.3.4 Quality of Clusters and Modularity Method 261 12.3.5 K-Nearest-Neighboring 263 12.4 Clustering Algorithms in Tag-Based Recommender Systems 264 12.5 Chapter Summary 266 Index 271
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spellingShingle Xu, Guandong
Zong, Yu
Yang, Zhenglu
Applied data mining
Datenaufbereitung (DE-588)4148865-9 gnd
Big Data (DE-588)4802620-7 gnd
Datenmanagement (DE-588)4213132-7 gnd
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subject_GND (DE-588)4148865-9
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(DE-588)4123037-1
title Applied data mining
title_auth Applied data mining
title_exact_search Applied data mining
title_full Applied data mining Guandong Xu ; Yu Zong ; Zhenglu Yang
title_fullStr Applied data mining Guandong Xu ; Yu Zong ; Zhenglu Yang
title_full_unstemmed Applied data mining Guandong Xu ; Yu Zong ; Zhenglu Yang
title_short Applied data mining
title_sort applied data mining
topic Datenaufbereitung (DE-588)4148865-9 gnd
Big Data (DE-588)4802620-7 gnd
Datenmanagement (DE-588)4213132-7 gnd
Datenanalyse (DE-588)4123037-1 gnd
topic_facet Datenaufbereitung
Big Data
Datenmanagement
Datenanalyse
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