Clustering ensemble algorithm with high-order consistency learning

TL;DR

This paper introduces a high-order consensus learning algorithm for clustering ensembles that improves accuracy and mutual information by effectively fusing multiple high-order data connections, outperforming existing methods.

Contribution

The paper proposes a novel high-order information fusion algorithm for clustering ensembles that enhances performance by integrating multiple data connection dimensions.

Findings

Clustering accuracy improved by an average of 7.22%.

Normalized Mutual Information increased by an average of 9.19%.

Outperforms existing clustering ensemble algorithms.

Abstract

Most of the research on clustering ensemble focuses on designing practical consistency learning algorithms.To solve the problems that the quality of base clusters varies and the low-quality base clusters have an impact on the performance of the clustering ensemble, from the perspective of data mining, the intrinsic connections of data were mined based on the base clusters, and a high-order information fusion algorithm was proposed to represent the connections between data from different dimensions, namely Clustering Ensemble with High-order Consensus learning (HCLCE). Firstly, each high-order information was fused into a new structured consistency matrix. Then, the obtained multiple consistency matrices were fused together. Finally, multiple information was fused into a consistent result. Experimental results show that LCLCE algorithm has the clustering accuracy improved by an average of 7.22%, and the Normalized Mutual Information (NMI) improved by an average of 9.19% compared with the suboptimal Locally Weighted Evidence Accumulation (LWEA) algorithm. It can be seen that the proposed algorithm can obtain better clustering results compared with clustering ensemble algorithms and using one information alone.