A Method for Dynamic Clustering of Data

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.

• Type: Conference
• Year: 1998
• Where: BMVC
• Authors: Arnaldo J. Abrantes, Jorge S. Marques
• Keywords: BMVC 1998, Computer Vision, Constrained Clustering, Dynamic Clustering Algorithm, Static Clustering