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Association Rule Mining
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Models and Algorithms
Paperback
Print on Demand | Lieferzeit: Print on Demand - Lieferbar innerhalb von 3-5 Werktagen I
Artikel-Nr:
9783540435334
Veröffentl:
2002
Einband:
Paperback
Erscheinungsdatum:
10.04.2002
Seiten:
256
Autor:
Shichao Zhang
Gewicht:
391 g
Format:
233x155x15 mm
Serie:
2307, Lecture Notes in Artificial Intelligence
Sprache:
Englisch
Beschreibung:
Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention.
The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professio- nals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quan- titative association rules, causal rules, exceptional rules, ne- gative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowled- ge discovery in databases, and anyone who is interested in asso- ciation rule mining.
Includes supplementary material: sn.pub/extras
Association Rule.- Negative Association Rule.- Causality in Databases.- Causal Rule Analysis.- Association Rules in Very Large Databases.- Association Rules in Small Databases.- Conclusion and Future Work.