SIGMOD'2000 Paper ID: 196 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 long patterns. In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended pre xtree structure for storing compressed, crucial information about frequent patterns, and develop an e cient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. E ciency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-treebased mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based divide-and-conquer method is used to dramatically reduce the search space. Our performance study shows that the FP-growth method is e cient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.
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6
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is a professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, stream data mining, spatiotemporal and multimedia data mining, biological data mini
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- 2005
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3
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Depth- rst generation of large itemsets for association rules
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1
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Data Engineering (ICDE’01
– Conf
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1
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received his M.Sc. degree in Computing Science at Simon Fraser University in 2001 and has been working as a software engineering in B.C
– Yin
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