Amorim, RC (2016) A survey on feature weighting based K-Means algorithms. Journal of Classification, 33 (2). pp. 210-242. DOI https://doi.org/10.1007/s00357-016-9208-4
Amorim, RC (2016) A survey on feature weighting based K-Means algorithms. Journal of Classification, 33 (2). pp. 210-242. DOI https://doi.org/10.1007/s00357-016-9208-4
Amorim, RC (2016) A survey on feature weighting based K-Means algorithms. Journal of Classification, 33 (2). pp. 210-242. DOI https://doi.org/10.1007/s00357-016-9208-4
Abstract
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 |
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Additional Information: | Journal of Classification (to appear) |
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 Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Sep 2017 15:05 |
Last Modified: | 16 May 2024 17:45 |
URI: | http://repository.essex.ac.uk/id/eprint/20367 |
Available files
Filename: 1601.03483v1.pdf