open access

https://hdl.handle.net/2115/47465

Frequent closed item set mining based on zero-suppressed BDDs

Frequent Closed Item Set Mining Based on Zero-suppressed BDDs

英語

closed pattern

2007-11-01

人工知能学会

Transactions of the Japanese Society for Artificial Intelligence : AI

22(2)

165-172

論文特集:データマイニングと統計数理

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.

学術雑誌論文

Version of Record

1346-0714