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A survey on feature selection approaches for clustering

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Abstract

The massive growth of data in recent years has led challenges in data mining and machine learning tasks. One of the major challenges is the selection of relevant features from the original set of available features that maximally improves the learning performance over that of the original feature set. This issue attracts researchers’ attention resulting in a variety of successful feature selection approaches in the literature. Although there exist several surveys on unsupervised learning (e.g., clustering), lots of works concerning unsupervised feature selection are missing in these surveys (e.g., evolutionary computation based feature selection for clustering) for identifying the strengths and weakness of those approaches. In this paper, we introduce a comprehensive survey on feature selection approaches for clustering by reflecting the advantages/disadvantages of current approaches from different perspectives and identifying promising trends for future research.

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  1. Department of Computer Technology and Information Systems, Mehmet Akif Ersoy University, Burdur, Turkey

    Emrah Hancer

  2. Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Bing Xue & Mengjie Zhang

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  1. Emrah Hancer
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Hancer, E., Xue, B. & Zhang, M. A survey on feature selection approaches for clustering. Artif Intell Rev 53, 4519–4545 (2020). https://doi.org/10.1007/s10462-019-09800-w

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