Mining frequent patterns without candidate generation
Jiawei HanSchool of Computing Science, Simon Fraser UniversityJian PeiSchool of Computing Science, Simon Fraser UniversityYiwen YinSchool of Computing Science, Simon Fraser University
2000en
ABI
Abstract
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 prolific patterns and/or long patterns.
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