Comparison of hierarchical cluster analysis methods by cophenetic correlation

Abstract

This study proposes the best clustering method(s) for different distance measures under two different conditions using the cophenetic correlation coefficient.

In the first one, the data has multivariate standard normal distribution without outliers for [InlineEquation not available: see fulltext.] and the second one is with outliers (5%) for [InlineEquation not available: see fulltext.]. The proposed method is applied to simulated multivariate normal data via MATLAB software.

According the results of simulation the Average (especially for [InlineEquation not available: see fulltext.]) and Centroid (especially for [InlineEquation not available: see fulltext.] and [InlineEquation not available: see fulltext.]) methods are recommended at both conditions.

This study hopes to contribute to literature for making better decisions on selection of appropriate cluster methods by using subgroup sizes, variable numbers, subgroup means and variances.[PUBLICATION ABSTRACT]