Projective clustering ensembles
Data Mining and Knowledge Discovery
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Projective Clustering Ensembles (PCE) has recently been formulated to solve the problem of deriving a robust projective consensus clustering from an ensemble of projective clustering solutions. PCE is formalized as an optimization problem with either a two-objective or a single-objective function, depending on whether the object-based and the feature-based representations of the clusters in the ensemble are treated separately. A major result in is that single-objective PCE outperforms two-objective PCE in terms of efficiency, at the cost of lower accuracy in consensus clustering. In this paper, we enhance the single-objective PCE formulation, with the ultimate goal of providing more effective formulations capable of reducing the accuracy gap with the two-objective counterpart, while maintaining the efficiency advantages. We provide theoretical insights into the single-objective function, and introduce two heuristics that overcome the major limitations of the previous single-objective PCE formulation. Experimental evidence has demonstrated the significance of our proposed heuristics. In fact, results have not only confirmed a far better efficiency w.r.t. two-objective PCE, but have also shown the claimed improvements in accuracy of the consensus clustering obtained by the new single-objective PCE.