Comparison of hierarchical cluster analysis methods by cophenetic correlation

Abstract

Purpose: This study proposes the best clustering method(s) for different distance measures under two different conditions using the cophenetic correlation coefficient. Methods: In the first one, the data has multivariate standard normal distribution without outliers for n = 10, 50, 100 and the second one is with outliers (5%) for n = 10, 50, 100. The proposed method is applied to simulated multivariate normal data via MATLAB software. Results: According the results of simulation the Average (especially for n = 10) and Centroid (especially for n = 50 and n = 100) methods are recommended at both conditions. Conclusions: 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. © 2013 Saraçli et al.; licensee Springer.