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A survey on feature weighting based K-Means algorithms

Amorim, RC (2016) 'A survey on feature weighting based K-Means algorithms.' Journal of Classification, 33 (2). 210 - 242. ISSN 0176-4268

1601.03483v1.pdf - Accepted Version

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.

Item Type: Article
Uncontrolled Keywords: Feature weighting, K-Means, Partitional clustering, Feature selection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 18 Sep 2017 15:05
Last Modified: 09 Nov 2018 11:15

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