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Metric incremental clustering of nominal data

  • University of Massachusetts Boston
  • Karlsruhe Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an algorithm for clustering nominal data that is based on a metric on the set of partitions of a finite set of objects; this metric is defined starting from a lower valuation of the lattice of partitions. The proposed algorithm seeks to determine a clustering partition such that the total distance between this partition and the partitions determined by the attributes of the objects has a local minimum. The resulting clustering is quite stable relative to the ordering of the objects.

Original languageEnglish
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages523-526
Number of pages4
DOIs
StatePublished - 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: Nov 1 2004Nov 4 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Conference

ConferenceProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/TerritoryUnited Kingdom
CityBrighton
Period11/1/0411/4/04

ASJC Scopus Subject Areas

  • General Engineering

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