Consensus Clustering With Co-Association Matrix Optimization

Yifan Shi

doi:10.1109/TNNLS.2022.3201975

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Consensus Clustering With Co-Association Matrix Optimization

2024-03-01

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Yifan Shi

Zhiwen Yu *

陈晨 (C. L. Philip Chen)

Huanqiang Zeng

DOI：10.1109/TNNLS.2022.3201975

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Abstract

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Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization; 2) the information from the original dataset is rarely considered; and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.

Keywords:

Optimization

Clustering algorithms

Partitioning algorithms

Linear programming

Correlation

Clustering methods

Stochastic processes

Alternative optimization

consensus clustering