Semi-supervised Clustering Ensemble by Voting

TL;DR

This paper enhances clustering ensemble methods by integrating semi-supervised techniques and a flexible weighting mechanism, improving the accuracy and adaptability of consensus clustering results.

Contribution

It introduces a semi-supervised approach with a novel weighting mechanism for ensemble generation and consensus, advancing clustering ensemble techniques.

Findings

Improved clustering accuracy with semi-supervised ensemble methods

Flexible weighting enhances compatibility and user feedback integration

Demonstrated effectiveness on benchmark datasets

Abstract

Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of the same clustering algorithm for the same data set, this is accomplished using on a consensus function, the efficiency and accuracy of this method has been proven in many works in literature. In the first part of this paper we make a comparison among current approaches to clustering ensemble in literature. All of these approaches consist of two main steps: the ensemble generation and consensus function. In the second part of the paper, we suggest engaging supervision in the clustering ensemble procedure to get more enhancements on the clustering results. Supervision can be applied in two places: either by using semi-supervised algorithms in the clustering ensemble generation step or in the form of a feedback used by the consensus function stage. Also, we introduce a flexible two parameter weighting mechanism, the first parameter describes the compatibility between the datasets under study and the semi-supervised clustering algorithms used to generate the base partitions, the second parameter is used to provide the user feedback on the these partitions. The two parameters are engaged in a "relabeling and voting" based consensus function to produce the final clustering.