2024-03-28T08:31:04Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/474652022-11-17T02:08:08Zhdl_2115_20053hdl_2115_145Frequent closed item set mining based on zero-suppressed BDDsFrequent Closed Item Set Mining Based on Zero-suppressed BDDsMINATO, Shin-ichiARIMURA, Hirokidata miningitem setBDDZBDDclosed pattern007Frequent 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.人工知能学会Journal Articleapplication/pdfhttp://hdl.handle.net/2115/47465https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/47465/1/30_22_165.pdf1346-0714人工知能学会論文誌2221651722007-11-01enginfo:doi/10.1527/tjsai.22.165publisher