Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Multiview data analysis provides an effective means to integrate the distinct information sources which are inherent to many applications. Data clustering in a multiview setting specifically aims to identify the most appropriate grouping for a collection of entities, where those entities (or their relationships) can be described from multiple perspectives. Leveraging recent advances in multiobjective clustering, we propose a new evolutionary method to tackle this challenge. Designed around a flexible and unbiased solution representation, together with strategies based on the minimum spanning tree and neighborhood relations, our algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate tradeoffs between the data views. Through a series of experiments, we investigate the suitability of our proposal in the context of a bioinformatics application, clustering of plausible protein structures, and a diverse set of synthetic problems. The specific case of two data views is considered in this article. The evaluation with respect to a variety of reference approaches demonstrates the effectiveness of our method in discovering high-quality partitions in a multiview setting. Robustness against unreliable data sources and the ability to automatically determine the number of clusters are additional advantages evidenced by the results obtained.

Automated summary

Multiview data analysis integrates distinct information sources in many applications. Data clustering in a multiview setting aims to group entities based on multiple perspectives. This article proposes a new evolutionary method for multiview clustering, optimizing multiple objectives simultaneously. Experimental evaluation demonstrates the effectiveness of the proposed method in discovering high-quality partitions, considering a bioinformatics application and various synthetic problems. The method also exhibits robustness against unreliable data sources and automatic determination of the number of clusters.