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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

Keyword(s): k-means, Machine Learning, Data Aggregations.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Industrial Applications of Artificial Intelligence ; Sensor Networks ; Signal Processing ; Soft Computing

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
El malki, N.; Ravat, F. and Teste, O. (2019). k-means Improvement by Dynamic Pre-aggregates. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 133-140. DOI: 10.5220/0007675201330140

@conference{iceis19,
author={Nabil {El malki}. and Franck Ravat. and Olivier Teste.},
title={k-means Improvement by Dynamic Pre-aggregates},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={133-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007675201330140},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - k-means Improvement by Dynamic Pre-aggregates
SN - 978-989-758-372-8
IS - 2184-4984
AU - El malki, N.
AU - Ravat, F.
AU - Teste, O.
PY - 2019
SP - 133
EP - 140
DO - 10.5220/0007675201330140
PB - SciTePress