Frequent Pattern Mining Algorithms for Data Clustering

Publikation: Bidrag til bog/antologi/rapport/proceedingBidrag til bog/antologiForskningpeer review

  • Arthur Zimek, Ludwig-Maximilians-Universität München, Tyskland
  • Ira Assent
  • Jilles Vreeken , Max-Planck Institute for Informatics and Saarland University, Tyskland
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
OriginalsprogEngelsk
TitelFrequent Pattern Mining
RedaktørerCharu C. Aggarwal, Jiawei Han
Antal sider21
ForlagSpringer
Udgivelsesår2014
Sider403-423
ISBN (trykt)978-3-319-07820-5
ISBN (Elektronisk)978-3-319-07821-2
DOI
StatusUdgivet - 2014

    Forskningsområder

  • Subspace clustering, Monotonicity, Redundancy

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