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Frequent closed item set mining based on zero-suppressed BDDs

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Title: Frequent closed item set mining based on zero-suppressed BDDs
Authors: MINATO, Shin-ichi Browse this author →KAKEN DB
ARIMURA, Hiroki Browse this author
Keywords: data mining
item set
closed pattern
Issue Date: 1-Nov-2007
Publisher: 人工知能学会
Journal Title: 人工知能学会論文誌
Journal Title(alt): Transactions of the Japanese Society for Artificial Intelligence : AI
Volume: 22
Issue: 2
Start Page: 165
End Page: 172
Publisher DOI: 10.1527/tjsai.22.165
Abstract: Frequent item set mining is one of the fundamental techniques for knowledge discovery and data mining. In the last decade, a number of efficient algorithms for frequent item set mining have been presented, but most of them focused on just enumerating the item set patterns which satisfy the given conditions, and it was a different matter how to store and index the result of patterns for efficient data analysis. Recently, we proposed a fast algorithm of extracting all frequent item set patterns from transaction databases and simultaneously indexing the result of huge patterns using Zero-suppressed BDDs (ZBDDs). That method, ZBDD-growth, is not only enumerating/listing the patterns efficiently, but also indexing the output data compactly on the memory to be analyzed with various algebraic operations. In this paper, we present a variation of ZBDD-growth algorithm to generate frequent closed item sets. This is a quite simple modification of ZBDD-growth, and additional computation cost is relatively small compared with the original algorithm for generating all patterns. Our method can conveniently be utilized in the environment of ZBDD-based pattern indexing.
Description: 論文特集:データマイニングと統計数理
Type: article
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 湊 真一

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