A clustering framework based on subjective and objective validity criteria
Maria Halkidi, Dimitrios Gunopulos, Michael Vazirgiannis, Naveen Kumar V, Carlotta Domeniconi · ACM Transactions on Knowledge Discovery from Data · 2008
Clustering, as an unsupervised learning process is a challenging problem, especially in cases of high-dimensional datasets. Clustering result quality can benefit from user constraints and objective validity assessment. In this article, we propose a semisupervised framework for learning the weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on: (i) user constraints; and (ii) the quality of intermediate clustering results in terms of their structural properties. The proposed framework uses the clustering algorithm and the validity measure as its parameters. We develop and discuss algorithms for learning and tuning the weights of contributing dimensions and defining the “best” clustering obtained by satisfying user constraints. Experimental results on benchmark datasets demonstrate the superiority of the proposed approach in terms of improved clustering accuracy.