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Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge.

Amorim, Renato (2008) Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge. In: Second International Conference on Advanced Engineering Computing and Applications in Sciences, 2008-09-29 - 2008-10-04, Valencia, Spain.

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Abstract

It is here presented a new method for clustering that uses very limited amount of labeled data, employees two pairwise rules, namely must link and cannot link and a single wise one, cannot cluster. It is demonstrated that the incorporation of these rules in the intelligent k-means algorithm may increase the accuracy of results, this is proven with experiments where the real number of clusters in the data is unknown to the method.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Jan 2022 10:08
Last Modified: 27 Jan 2022 10:08
URI: http://repository.essex.ac.uk/id/eprint/32142

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