Comparison of hierarchical cluster analysis methods by cophenetic correlation

Sinan Saraçli,Nurhan Doğan,İsmet Doğan

doi:10.1186/1029-242x-2013-203

Sinan Saraçli, Nurhan Doğan + Show 1 more

Open Access

https://doi.org/10.1186/1029-242x-2013-203

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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 and the second one is with outliers (5%) for . The proposed method is applied to simulated multivariate normal data via MATLAB software. According the results of simulation the Average (especially for ) and Centroid (especially for and ) 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.

Highlights

Classification, in its widest sense, has to do with forms of the relatedness and with the organization and display of the relations in a useful manner
Ward’s method performed significantly better than the other clustering procedures and average linkage gave relatively poor results
2 Method In this study, seven cluster analysis methods are compared by the cophenetic correlation coefficient computed according to different clustering methods with a sample size (n =, n = and n = ), variables number (x =, x = and x = ) and distance measures via a simulation study