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Association Rule Mining [electronic resource] :Models and Algorithms /

Contributor(s): Zhang, Chengqi [editor.] | Zhang, Shichao [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 2307Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2002.Description: XII, 244 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540460275.Subject(s): Computer science | Algorithms | Database management | Information storage and retrieval | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Database Management | Information Storage and Retrieval | Algorithm Analysis and Problem ComplexityOnline resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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