Date: Mon Apr 10 20:29:57 2006
Authors: Shin-ichi Minato and Hiroki Arimura
Abstract. Frequent pattern mining is one of the fundamental techniques for knowledge discovery and data mining. In the last decade, a number of efficient algorithms for frequent pattern mining have been presented, but most of them focused on just enumerating the 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. In this paper, we propose a fast algorithm of extracting all/maximal frequent patterns from transaction databases and simultaneously indexing the result of huge patterns using Zero-suppressed BDDs (ZBDDs). Our method, ZBDD-growth, is fast as competitive to the existing state-of-the-art algorithms, and not only enumerating/listing the patterns but also indexing the output data compactly on the memory. After mining, the result of patterns can efficiently be analyzed by using algebraic operations. The data structures of BDDs have already been used in VLSI logic design systems successively, but our method will be the first practical work of applying the BDD-based techniques for data mining area.
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