Comparison between compactness and connectedness criteria in data clustering

Abstract

Data clustering is the first step in data mining. It aims at finding homogeneous groups of objects based on the degree of similarity and dissimilarity of their attributes. Most of the existing clustering methods are based on a single criterion to measure the goodness of clusters. In most cases, these methods are not suitable for different types of datasets with different characteristics. In this study, biogeography-based optimisation BBO and great deluge GD algorithms are combined to address the data clustering as single objective optimisation problem; two versions of the proposed approach that employed two different clustering criteria as the objective function have been investigated using fourteen 2D synthetic benchmark datasets. The quality of the obtained clusters of both versions of the proposed approach is insufficient with respect to the external evaluation function i.e. F-measure. Thus, the data-clustering problem preferred to be tackled as multi-objective clustering algorithms.