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Constrained Clustering with Minkowski Weighted K-Means

Amorim, RC (2013) Constrained Clustering with Minkowski Weighted K-Means. In: 13th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), 2012, 2012-11-20 - 2012-11-22, Budapest.

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In this paper we introduce the Constrained Minkowski Weighted K-Means. This algorithm calculates cluster specific feature weights that can be interpreted as feature rescaling factors thanks to the use of the Minkowski distance. Here, we use an small amount of labelled data to select a Minkowski exponent and to generate clustering constrains based on pair-wise must-link and cannot-link rules. We validate our new algorithm with a total of 12 datasets, most of which containing features with uniformly distributed noise. We have run the algorithm numerous times in each dataset. These experiments ratify the general superiority of using feature weighting in K-Means, particularly when applying the Minkowski distance. We have also found that the use of constrained clustering rules has little effect on the average proportion of correctly clustered entities. However, constrained clustering does improve considerably the maximum of such proportion.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 13th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), 2012
Uncontrolled Keywords: Minkowski Weighted K-Means, Constrained K-Means, Minkowski metric, semi-supervised learning, feature weighting
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 14:30
Last Modified: 18 Oct 2017 16:16

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