Title of Paper:WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory

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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:IEEE TRANSACTIONS ON CYBERNETICS

Key Words:Cluster ensemble pairwise constraints semisupervised clustering the wisdom of crowds (WOCs)

Abstract:The wisdom of crowds (WOCs), as a theory in the social science, gets a new paradigm in computer science. The WOC theory explains that the aggregate decision made by a group is often better than those of its individual members if specific conditions are satisfied. This paper presents a novel framework for unsupervised and semisupervised 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 constructing of individual clustering results and final combination for clustering ensemble. First, independency criterion, as a novel mapping system on the raw data set, removes the correlation between features on our proposed method. Then, decentralization as a novel mechanism generates high quality individual clustering results. Next, uniformity as a new diversity metric evaluates the generated clustering results. Further, weighted evidence accumulation clustering method is proposed for the final aggregation without using thresholding procedure. Experimental study on varied data sets demonstrates that the proposed approach achieves superior performance to state-of-the-art methods.

ISSN No.:2168-2267

Translation or Not:no

Date of Publication:2018-02-01

Co-author:Sheng Jun Huang,zdq

Correspondence Author:YOUSEFNEZHAD MUHAMMAD