Abstract:
Mining frequent patterns in transaction databases, timeseries 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 proli#c patterns and#or 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, whichavoids costly, repeated database scans, #2# our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large n...
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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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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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