Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Recovery Levels of Clustering Algorithms Using Different Similarity Measures for Functional Data

  • Chae, Seong San (Department of Information and Statistics, Daejeon University) ;
  • Kim, Chansoo (Department of Statistics, Oklahoma State University) ;
  • Warde, William D. (Department of Statistics, Oklahoma State university)
  • Published : 2004.08.01
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Abstract

Clustering algorithms with different similarity measures are commonly used to find an optimal clustering or close to original clustering. The recovery level of using Euclidean distance and distances transformed from correlation coefficients is evaluated and compared using Rand's (1971) C statistic. The C values present how the resultant clustering is close to the original clustering. In simulation study, the recovery level is improved by applying the correlation coefficients between objects. Using the data set from Spellman et al. (1998), the recovery levels with different similarity measures are also presented. In general, the recovery level of true clusters was increased by using the correlation coefficients.

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