Frequent Closed Item Set Mining Based on Zero-suppressed BDDs

Abstract

keywords: data mining, item set, BDD, ZBDD, closed patternSummaryFrequent item set mining is one of the fundamental techniques for knowledge discovery and data min-ing. 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, andit 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 andsimultaneously 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 datacompactly on thememory to be analyzed with variousalgebraic operations. In this paper, we present a vari-ation of ZBDD-growth algorithm to generate frequent closed item sets. This is a quite simple modificationof ZBDD-growth, and additional computation cost is relatively small compared with the original algorithmfor generating all patterns. Our method can conveniently be utilized in the environment of ZBDD-basedpattern indexing.