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The Wisdom of Crowds (WOC), as a theory in the social science, gets a newparadigm in computer science. The WOC theory explains that the aggregatedecision made by a group is often better than those of its individual membersif specific conditions are satisfied. This paper presents a novel framework forunsupervised and semi-supervised cluster ensemble by exploiting the WOC theory.We employ four conditions in the WOC theory, i.e., diversity, independency,decentralization and aggregation, to guide both the constructing of individualclustering results and the final combination for clustering ensemble. Firstly,independency criterion, as a novel mapping system on the raw data set, removesthe correlation between features on our proposed method. Then, decentralizationas a novel mechanism generates high-quality individual clustering results.Next, uniformity as a new diversity metric evaluates the generated clusteringresults. Further, weighted evidence accumulation clustering method is proposedfor the final aggregation without using thresholding procedure. Experimentalstudy on varied data sets demonstrates that the proposed approach achievessuperior performance to state-of-the-art methods.

WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory