Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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On the clustering of huge categorical data

  • Kim, Dae-Hak (School of Liberal Arts, Catholic University of Daegu)
  • Received : 2010.10.23
  • Accepted : 2010.11.23
  • Published : 2010.11.30
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Abstract

Basic objective in cluster analysis is to discover natural groupings of items. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input data. Various measures of similarities between objects are developed. In this paper, we consider a clustering of huge categorical real data set which shows the aspects of time-location-activity of Korean people. Some useful similarity measure for the data set, are developed and adopted for the categorical variables. Hierarchical and nonhierarchical clustering method are applied for the considered data set which is huge and consists of many categorical variables.

Keywords

References

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