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Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach (2004)

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by Jiawei Han , Jian Pei , Yiwen Yin , Runying Mao
Venue:DATA MINING AND KNOWLEDGE DISCOVERY
Citations:1750 - 64 self
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BibTeX

@MISC{Han04miningfrequent,
    author = {Jiawei Han and Jian Pei and Yiwen Yin and Runying Mao},
    title = { Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach},
    year = {2004}
}

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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 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-tree- based 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 generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient 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

Keyphrases

frequent pattern    candidate generation    frequent-pattern tree approach    large number    candidate set    costly generation    divide-and-conquer method    conditional database    long pattern    pattern-fragment growth method    repeated database scan    search space    performance study    previous study    short frequent pattern    fp-tree-based mining    novel frequent-pattern tree    mining method    crucial information    mining task    data structure    many kind    pattern fragment growth    new frequent-pattern mining method    apriori algorithm    apriori-like candidate    fp-growth method    extended prefix-tree structure    data mining research    transaction database    large database    generation-and-test approach    complete set    time-series database   

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