PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth (2001)
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@INPROCEEDINGS{Pei01prefixspan:mining,
author = {Jian Pei and Jiawei Han and Behzad Mortazavi-asl and Helen Pinto and Qiming Chen and Umeshwar Dayal and Mei-chun Hsu},
title = {PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth},
booktitle = {},
year = {2001},
pages = {215--224}
}
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Abstract
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 which may substantially reduce the number of combinations to be examined. However, still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long.
