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Université de Toulouse, UT2, IRIT(CNRS/UMR5505), Toulouse, France, Capgemini, 109 Avenue du Général Eisenhower, Toulouse and France

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1 Papers

k-means Improvement by Dynamic Pre-aggregates

Topics: Industrial Applications of Artificial Intelligence; Data Mining

Authors: Nabil El malki ¹ , Franck Ravat ² and Olivier Teste ³

Affiliations: ¹ Université de Toulouse, UT2, IRIT(CNRS/UMR5505), Toulouse, France, Capgemini, 109 Avenue du Général Eisenhower, Toulouse and France , ² Université de Toulouse, UT2, IRIT(CNRS/UMR5505), Toulouse and France , ³ Capgemini, 109 Avenue du Général Eisenhower, Toulouse and France

Digital Object Identifier: 10.5220/0007675201330140

Proceedings of ICEIS 2019, ISBN: 978-989-758-372-8

Quick Abstract

The k-means algorithm is one well-known of clustering algorithms. k-means requires iterative and repetitive accesses to data up to performing the same calculations several times on the same data. However, intermediate results that are difficult to predict at the beginning of the k-means process are not recorded to avoid recalculating some data in subsequent iterations. These repeated calculations can be costly, especially when it comes to clustering massive data. In this article, we propose to extend the k-means algorithm by introducing pre-aggregates. These aggregates can then be reused to avoid redundant calculations during successive iterations. We show the interest of the approach by several experiments. These last ones show that the more the volume of data is important, the more the pre-aggregations speed up the algorithm.

Related Ontology Subjects/Areas/Topics (9)

Artificial Intelligence	Databases and Information Systems Integration	Sensor Networks
Artificial Intelligence and Decision Support Systems	Enterprise Information Systems	Signal Processing
Data Mining	Industrial Applications of Artificial Intelligence	Soft Computing