Approaches for pattern discovery using sequential data mining

Manish Gupta, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the most influential methods for sequential pattern mining. There is also a vertical format based method which works on a dual representation of the sequence database. Work has also been done for mining patterns with constraints, mining closed patterns, mining patterns from multidimensional databases, mining closed repetitive gapped subsequences, and other forms of sequential pattern mining. Some works also focus on mining incremental patterns and mining from stream data. We present at least one method of each of these types and discuss their advantages and disadvantages. We conclude with a summary of the work.

Original languageEnglish (US)
Title of host publicationPattern Discovery Using Sequence Data Mining
Subtitle of host publicationApplications and Studies
PublisherIGI Global
Pages137-154
Number of pages18
ISBN (Print)9781613500569
DOIs
StatePublished - 2011

ASJC Scopus subject areas

  • Social Sciences(all)

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