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SIGMOD
2000
ACM

Mining Frequent Patterns without Candidate Generation

9 years 1 months ago
Mining Frequent Patterns without Candidate Generation
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-treebased mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generati...
Jiawei Han, Jian Pei, Yiwen Yin
Added 01 Aug 2010
Updated 01 Aug 2010
Type Conference
Year 2000
Where SIGMOD
Authors Jiawei Han, Jian Pei, Yiwen Yin
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