Incremental procedures for partitioning highly intermixed multi-class datasets into hyper-spherical and hyper-ellipsoidal clusters

Qinglu Kong, Qiuming Zhu

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Two procedures for partitioning large collections of highly intermixed datasets of different classes into a number of hyper-spherical or hyper-ellipsoidal clusters are presented. The incremental procedures are to generate a minimum numbers of hyper-spherical or hyper-ellipsoidal clusters with each cluster containing a maximum number of data points of the same class. The procedures extend the move-to-front algorithms originally designed for construction of minimum sized enclosing balls or ellipsoids for dataset of a single class. The resulting clusters of the dataset can be used for data modeling, outlier detection, discrimination analysis, and knowledge discovery.

Original languageEnglish (US)
Pages (from-to)457-477
Number of pages21
JournalData and Knowledge Engineering
Volume63
Issue number2
DOIs
Publication statusPublished - Nov 1 2007

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Keywords

  • Data clustering
  • Data models
  • Geometrical approximation
  • Knowledge discovery
  • Mini-max partition

ASJC Scopus subject areas

  • Information Systems and Management

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