Reference vector-based multi-objective clustering for high-dimensional data

doi:10.1016/j.asoc.2019.02.043

Reference vector-based multi-objective clustering for high-dimensional data

2019-05-01

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DOI：10.1016/j.asoc.2019.02.043

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Subspace clustering algorithms have shown their advantage in handling high-dimensional data by optimizing a linear combination of clustering criteria. However, setting the coefficients of these criteria items without prior knowledge will lead to inaccurate and poor robust clustering results. To address this problem, in this paper, we propose to optimize the multiple clustering criteria simultaneously without any predefined coefficients by a multi-objective evolutionary algorithm. Furthermore, to accelerate the convergence of the algorithm, we provide a novel local search method. In it, the multi-objective clustering problem is decomposed into many localized scalarizing sub-problems by reference vectors. Solutions are then locally searched around their associated sub-problems. Thirdly, we develop a knee-pruning fuzzy ensemble method for selecting the final solution. This method applies clustering ensemble in solutions selected from knee regions to get robust results. Experiments on UCI benchmarks and gene expression datasets show that our proposed algorithm can efficiently handle high-dimensional clustering problems without any user-defined coefficients. (C) 2019 Elsevier B.V. All rights reserved.

Keywords:

High-dimensional data

Multi-objective clustering

Soft subspace clustering

ESSC

Reference vector-based multi-objective clustering for high-dimensional data

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