Domain-Driven, Actionable Knowledge Discovery

Domain-Driven, Actionable Knowledge Discovery

Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure sup...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE intelligent systems 2007-07, Vol.22 (4), p.78-88, c3
Hauptverfasser: LONGBING CAO, QIANG YANG, BELL, David, NING ZHONG, ASHRAFI, Mafruz Zaman, TANIA, David, DUBOSSARSKY, Eugene
Format: Artikel
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Business
Business information
Computer science
control theory
systems
Data mining
data models
Data privacy
Data processing. List processing. Character string processing
Data security
Data visualization
database searching
Exact sciences and technology
Government
Humans
Infrastructure
Intelligent networks
Intelligent systems
Knowledge discovery
knowledge engineering
Knowledge management
Machine vision
Memory organisation. Data processing
R&D
Research & development
Research and development
Software
visualization
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2007.67