Evaluation framework of hierarchical clustering methods for binary data

The article aims to evaluate hierarchical clustering methods according to their performance for binary data type. We explore the accuracy of ten hierarchical clustering methods by experimenting with ten different distance measures. The three types of well, poorly and very poorly separated clusters of binary data sets are generated by selecting the appropriate parameters for binomial distribution and Monte Carlo method. In order to evaluate the precision of clustering methods the binary data sets are transformed to distance matrices. The error level each method is explored in relationship to distance measures, cluster types and data distributions. The Complete linkage, Flexible-beta and Ward´s methods have best clustering performance for the case of two well separated clusters of binary data.