Frequent pattern mining algorithms for data clustering

Arthur Zimek*, Ira Assent, Jilles Vreeken

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say that frequent pattern mining was at the cradle of subspace clustering-yet, it quickly developed into an independent research field. In this chapter, we discuss how frequent pattern mining algorithms have been extended and generalized towards the discovery of local clusters in high-dimensional data. In particular, we discuss several example algorithms for subspace clustering or projected clustering as well as point out recent research questions and open topics in this area relevant to researchers in either clustering or pattern mining.

Original languageEnglish
Title of host publicationFrequent Pattern Mining
EditorsC. Aggarwal, J. Han
PublisherSpringer
Publication dateJul 2014
Pages403-423
ISBN (Print)978-3-319-07820-5
ISBN (Electronic)978-3-319-07821-2
DOIs
Publication statusPublished - Jul 2014
Externally publishedYes

Keywords

  • Monotonicity
  • Redundancy
  • Subspace clustering

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