Quality-Driven Hierarchical Clustering Algorithm for Service Intelligence Computation

Abstract

Clustering is an important technique for intelligence computation such as trust, recommendation, reputation, and requirement elicitation. With the user centric nature of service and the user's lack of prior knowledge on the distribution of the raw data, one challenge is on how to associate user quality requirements on the clustering results with the algorithmic output properties (e.g. number of clusters to be targeted). In this paper, we focus on the hierarchical clustering process and propose two quality-driven hierarchical clustering algorithms, HBH (homogeneity-based hierarchical) and HDH (homogeneity-driven hierarchical) clustering algorithms, with minimum acceptable homogeneity and relative population for each cluster output as their input criteria. Furthermore, we also give a HDH-approximation algorithm in order to address the time performance issue. Experimental study on data sets with different density distribution and dispersion levels shows that the HDH gives the best quality result and HDH-approximation can significantly improve the execution time.