Comparison Among Methods for k Estimation in k-means

Naldi, Murilo C., Fontana, Andre, and Campello, Ricardo J.G.B. (2009) Comparison Among Methods for k Estimation in k-means. In: Proceedings of the 9th International Conference on Intelligent Systems Design and Applications, pp. 1006-1013. From: ISDA 2009: 9th International Conference on Intelligent Systems Design and Applications, 30 November - 2 December 2009, Pisa, Italy.

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Abstract

One of the most influential algorithms in data mining, k-means, is broadly used in practical tasks for its simplicity, computational efficiency and effectiveness in high dimensional problems. However, k-means has two major drawbacks, which are the need to choose the number of clusters, k, and the sensibility to the initial prototypes' position. In this work, systematic, evolutionary and order heuristics used to suppress these drawbacks are compared. 27 variants of 4 algorithmic approaches are used to partition 324 synthetic data sets and the obtained results are compared.

Item ID: 47057
Item Type: Conference Item (Research - E1)
ISBN: 978-0-7695-3872-3
Funders: CAPES, CNPq, FAPESP
Date Deposited: 04 Jan 2017 08:04
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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