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PrefixSpan

swMATH ID: 20761
Software Authors: Pei J, Han J, Mortazavi Asl B, Pinto H, Chen Q, Dayal U, Hsu M C
Description: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of Apriori which may substantially reduce the number of combinations to be examined. However, Apriori still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. In this paper, we propose a novel sequential pattern mining method, called PrefixSpan (i.e., Prefix-projected Sequential pattern mining), which explores prefix-projection in sequential pattern mining. PrefixSpan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover, prefix-projection substantially reduces the size of projected databases and leads to efficient processing. Our performance study shows that PrefixSpan outperforms both the Apriori-based GSP algorithm and another recently proposed method, FreeSpan, in mining large sequence databases.PrefixSpan
Homepage: http://dl.acm.org/citation.cfm?id=879716
Related Software: SPADE; CloSpan; BIDE; gSpan; SPMF; CLOSET; CloseGraph; CMAR; C4.5; XPath; Xproj; CP-miner; LCM; Krimp; Minebench; PR-Miner; SAS/STAT; SAS; ROCK; TEIRESIAS
Cited in: 39 Documents

Standard Articles

1 Publication describing the Software Year
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
Pei, Jian; Han, Jiawei; Mortazavi-Asl, Behzad; Wang, Jianyong; Pinto, Helen; Chen, Qiming; Dayal, Umeshwar; Hsu, Mei-Chun
2004
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Cited by 130 Authors

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