A Method for Dynamic Clustering of Data | Zendy

Arnaldo J. Abrantes | Zendy; Jorge S. Marques | Zendy

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A Method for Dynamic Clustering of Data

Author(s) -

Arnaldo J. Abrantes,

Jorge S. Marques

Publication year - 1998

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.12.16

Subject(s) - cluster analysis , computer science , cure data clustering algorithm , fuzzy clustering , canopy clustering algorithm , data stream clustering , data mining , correlation clustering , robustness (evolution) , artificial intelligence , outlier , centroid , pattern recognition (psychology) , algorithm , biochemistry , chemistry , gene

This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Kohonen maps, elastic nets and fuzzy c-means). The work is based on an unified framework for constrained clustering recently proposed by the authors in [1]. This framework is extended by using a motion model for the clusters which includes global and local evolution of the data centroids. A noise model is also proposed to increase the robustness of the dynamic clustering algorithm with respect to outliers.

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