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DyClee: Dynamic clustering for tracking evolving environments
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DOI：10.1016/j.patcog.2019.05.024.png)
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Abstract
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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 mu-clusters. The density-based algorithm analyses the micro-clusters to provide the final clusters: thanks 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 about cluster convexity. It shows fast response to data streams and good outlier rejection properties. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Dynamic clustering
Data mining
On-line learning
Time-series
Data streams
Multi-density clustering
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