From clustering to clustering ensemble selection: A review

Abstract

Clustering, as an unsupervised learning, is aimed at discovering the natural groupings of a set of patterns, points, or objects. In clustering algorithms, a significant problem is the absence of a deterministic approach based on which users can decide which clustering method best matches a given set of input data. This is due to using certain criteria for optimization. Clustering ensemble as a knowledge reuse offers a solution to solve the challenges inherent in clustering. It seeks to explore results of high stability and robustness by composing computed solutions achieved by base clustering algorithms without getting access to the features. Combining base clusterings together degrades the quality of the final solution when low-quality ensemble members are used. Several researchers in this field have suggested the concept of clustering ensemble selection for the aim of selecting a subset of base clustering based on quality and diversity. While clustering ensemble makes a combination of all ensemble members, clustering ensemble selection chooses a subset of ensemble members and forms a smaller cluster ensemble that performs better than the clustering ensemble. This survey includes the historical development of data clustering that makes an overview on basic clustering techniques, discusses clustering ensemble algorithms including ensemble generation mechanisms and consensus function, and point out clustering ensemble selection techniques with considering quality and diversity.