Adaptive Hausdorff distances and dynamic clustering of symbolic interval data

This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocation algorithms involving the construction of the clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between the clusters and their corresponding representatives. In this paper, each pattern is represented by a vector of intervals. Adaptive Hausdorff distances are the measures used to compare two interval vectors. Adaptive distances at each iteration change for each cluster according to its intra-class structure. The advantage of these adaptive distances is that the clustering algorithm is able to recognize clusters of different shapes and sizes. To evaluate this method, experiments with real and synthetic interval data sets were...

• Type: Journal
• Year: 2006
• Where: PRL
• Authors: Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Marie Chavent, Yves Lechevallier
• Keywords: Adaptive Hausdorff Distances, Clustering Algorithm, Dynamic Clustering, PRL 2006