Weighted Cluster Ensemble Based on Partition Relevance Analysis With Reduction Step

Abstract

Over the last decade, the advent of the cluster ensemble framework has enabled more accurate and robust data analysis than traditional single clustering algorithms. The improved clustering of microarray data has had a particularly strong impact in the fields of genomics and medicine. However, when we bring several ensemble members together to form a consensus, low-quality data partitions can seriously compromise the final solution. One way to overcome this problem is the weighted cluster ensemble approach based on Partition Relevance Analysis (PRA), which uses internal cluster validity indices to evaluate and weight the ensemble members before the fusion. Unfortunately, the selection of appropriate validation indices for given data is far from trivial. In this paper, we propose an additional step in PRA that reduces the size of the committee of cluster validation indices. It does so by eliminating redundant and noisy indices using data dimensionality reduction methods. Our extension works in an unsupervised way, minimizing the amount of user intervention and required expert knowledge. We adapted three conventional consensus functions based on the principle of evidence accumulation to work with PRA weights. We demonstrate the advantages of the proposed reduction step of PRA based on extensive experiments with 25 gene expression and 15 non-genetic real-world datasets, where we compared 15 consensus functions.

Fuzzy Divergence Weighted Ensemble Clustering With Spectral Learning Based on Random Projections for Big Data

“…As a consequence, various FCM ensemble methods [2], employing shallow randomization-based techniques like RP, have been investigated. Among them, RP-based FCM clustering (RPFCM) [6] has garnered interest.…”

“…An intuitive method to create low-quality base clustering has been developed. Some methods have been developed to assess each ensemble member and assign it a weight to enhance consensus clustering [2]. Li et al designed multi-view clustering via a learned bipartite graph [3].…”