Cluster analysis using different correlation coefficients |
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Authors: | Seong S Chae Chansoo Kim Jong-Min Kim William D Warde |
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Institution: | (1) Department of Applied Statistics, Daejeon University, Daejeon, 300-716, Republic of Korea;(2) Department of Statistics, Oklahoma State University, Stillwater, OK 74078, USA;(3) University of Minnesota, Statistics Discipline, Morris, MN 56267, USA |
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Abstract: | Partitioning objects into closely related groups that have different states allows to understand the underlying structure
in the data set treated. Different kinds of similarity measure with clustering algorithms are commonly used to find an optimal
clustering or closely akin to original clustering. Using shrinkage-based and rank-based correlation coefficients, which are
known to be robust, the recovery level of six chosen clustering algorithms is evaluated using Rand’s C values. The recovery levels using weighted likelihood estimate of correlation coefficient are obtained and compared to the
results from using those correlation coefficients in applying agglomerative clustering algorithms.
This work was supported by RIC(R) grants from Traditional and Bio-Medical Research Center, Daejeon University (RRC04713, 2005)
by ITEP in Republic of Korea. |
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Keywords: | Agglomerative clustering algorithm Rand’ s C statistic Weighted likelihood estimate |
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