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K-means improvement by dynamic pre-aggregates

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.
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Submitted on : Friday, February 28, 2020 - 11:39:22 AM
Last modification on : Wednesday, June 9, 2021 - 10:00:32 AM
Long-term archiving on: : Friday, May 29, 2020 - 1:55:59 PM


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  • HAL Id : hal-02493880, version 1
  • OATAO : 24784


Nabil El Malki, Franck Ravat, Olivier Teste. K-means improvement by dynamic pre-aggregates. 21st International Conference on Enterprise Information Systems (ICEIS 2019), May 2019, Heraklion, Crete, Greece. pp.133-140. ⟨hal-02493880⟩



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