Skip to Main content Skip to Navigation
New interface
Conference papers

A novel algorithm for dynamic clustering: properties and performance

Abstract : In this paper, we present a dynamic clustering algorithm that efficiently deals with data streams and achieves several important properties which are not generally found together in the same algorithm. The dynamic clustering algorithm operates online in two different timescale stages, a fast distance-based stage that generates micro-clusters and a density-based stage that groups the micro-clusters according to their density and generates the final clusters. The algorithm achieves novelty detection and concept drift thanks to a forgetting function that allows micro-clusters and final clusters to appear, drift, merge, split or disappear. This algorithm has been designed to be able to detect complex patterns even in multi-density distributions and making no assumption of cluster convexity. The performance of the dynamic clustering algorithm is assessed theoretically through complexity analysis and empirically through a set of experiments.
Complete list of metadata

Cited literature [24 references]  Display  Hide  Download
Contributor : Louise Travé-Massuyès Connect in order to contact the contributor
Submitted on : Tuesday, April 16, 2019 - 11:12:49 AM
Last modification on : Tuesday, October 25, 2022 - 11:58:11 AM


Files produced by the author(s)



Nathalie Barbosa Roa, Louise Travé-Massuyès, Victor H Grisales. A novel algorithm for dynamic clustering: properties and performance. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Dec 2016, Anaheim, United States. pp.565-570, ⟨10.1109/ICMLA.2016.0099⟩. ⟨hal-02004417⟩



Record views


Files downloads