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Data Clustering Analysis, From Simple Groupings to Scalable Clustering with Constraints

Clustering is the problem of grouping data based on similarity. While this problem has attracted the attention of many researchers for many years, we are witnessing a resurgence of interest in new clustering techniques. In this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of clustering validation.

Citation

O. Zaiane, A. Foss. "Data Clustering Analysis, From Simple Groupings to Scalable Clustering with Constraints". Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Taipei, Taiwan, pp 137-201, May 2002.

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Category: In Conference
Web Links: Webdocs

BibTeX

@incollection{Zaiane+Foss:PAKDD02,
  author = {Osmar R. Zaiane and Andrew Foss},
  title = {Data Clustering Analysis, From Simple Groupings to Scalable
    Clustering with Constraints},
  Pages = {137-201},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining
    (PAKDD)},
  year = 2002,
}

Last Updated: February 03, 2020
Submitted by Sabina P

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