title: Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances.
abstract: The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed using artificial and real interval data sets.
authors.affiliation: Department of Signal Processing and Electronic Systems, École Supérieure d'Électricité (SUPÉLEC), 91190 Gif-sur-Yvette, France. Chantal.Hajjar@supelec.fr
authors.forename: Chantal
authors.initials: C
authors.lastname: Hajjar
authors.forename: Hani
authors.initials: H
authors.lastname: Hamdan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer
keywords: Clustering; Interval data; Mahalanobis distance; Self-organizing maps; Unsupervised learning