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Journal Articles Pattern Recognition Year : 2019

DyClee: Dynamic clustering for tracking evolving environments


Evolving environments challenge researchers with non stationary data flows where the concepts (or states) being tracked can change over time. This requires tracking algorithms suited to represent concept evolution and in some cases, e.g. real industrial environments, also suited to represent time dependent features. This paper proposes a unified approach to track evolving environments that uses a two-stages distance-based and density-based clustering algorithm. In this approach data samples are fed as input to the distance based clustering stage in an incremental, online fashion, and they are then clustered to form -clusters. The density-based algorithm analyses the micro-clusters to provide the final clusters: Thank to a forgetting process, clusters may emerge, drift, merge, split or disappear, hence following the evolution of the environment. This algorithm has proved to be able to detect high overlapping clusters even in multi-density distributions, making no assumption of clusters convexity. It shows fast response to data streams and good outlier rejection properties.
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Dates and versions

hal-02135580 , version 1 (03-06-2019)



Nathalie Barbosa Roa, Louise Travé-Massuyès, Victor Hugo Grisales. DyClee: Dynamic clustering for tracking evolving environments. Pattern Recognition, 2019, 94, pp.162-186. ⟨10.1016/j.patcog.2019.05.024⟩. ⟨hal-02135580⟩
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