Some computational issues in cluster analysis with no a priori metric

Dan Coleman, Xioapeng Dong, Johanna Hardin, David M Rocke, David L. Woodruff

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Recent interest in data mining and knowledge discovery underscores the need for methods by which patterns can be discovered in data without any prior knowledge of their existence. In this paper, we explore computational methods of finding clusters of multivariate data points when there is no metric given a priori. We are given a sample, X, of n points in R(p) that come from g distinct multivariate normal populations with unknown parameters each of which contributes in excess of p points. Based on the assumption that we are given the number of groups, g, and a computational budget of T seconds of computer time, the paper reviews choices for cluster finding that have been described in the literature and introduces a new method that is a structured combination of two of them. We investigate these algorithms on some real data sets and describe simulation experiments. A principal conclusion is strong support for the contention that a two-stage algorithm based on a combinatorial search followed by the EM algorithm is the best way to find clusters.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalComputational Statistics and Data Analysis
Volume31
Issue number1
DOIs
StatePublished - Jul 28 1999

Keywords

  • Cluster analysis
  • Data mining
  • EM algorithm
  • Mixture models

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

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