User centric homogeneity-based clustering approach for intelligence computation

Abstract

Clustering is a classic technique widely used in computation intelligence to study similarity measure among entities of interest. The output measurement of clustering, however, is often computation centric (e.g. number of peaks, K) instead of user centric (e.g. quality of the clusters). This creates a big gap between the algorithms and the users, in particular when they are applied to areas such as software services. To address this issue, we propose to use the expected homogeneity degree among entities within a given cluster as the input quality requirements specified by the users to drive the data clustering process. We evaluate the effectiveness of our proposal by modifying two most widely used clustering methods, K-means and hierarchical, according to the homogeneity degrees of the clustered output results. © 2011 Springer-Verlag.