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Mining sequential patterns by pattern-growth: The PrefixSpan approach (2004)
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| Venue: | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
| Citations: | 206 - 10 self |
BibTeX
@ARTICLE{Pei04miningsequential,
author = {Jian Pei and Jiawei Han and Behzad Mortazavi-Asl and Jianyong Wang and Helen Pinto and Qiming Chen and Umeshwar Dayal and Mei-Chun Hsu},
title = {Mining sequential patterns by pattern-growth: The PrefixSpan approach},
journal = {IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING},
year = {2004},
volume = {16},
number = {11},
pages = {2004}
}
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Abstract
Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [1] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan [8], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE [29] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with user-specified constraints. The high promise of the pattern-growth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.
Keyphrases
sequential pattern prefixspan approach projected database efficient mining frequent substructure sequential pattern mining method sequential pattern-growth approach priori-based algorithm gsp comprehensive performance study explosive number intermediate subsequence ordered growth long pattern initial study pattern-growth approach sequential pattern mining algorithm user-specified constraint candidate generation-and-test approach efficient method pattern growth-based sequential pattern mining sequence database sequential pattern mining difficult problem numerous pattern frequent fragment frequent pattern vertical data format mining methodology important data mining problem broad application large sequence database pseudoprojection technique high promise
