Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A comparison of cluster validity criteria for a mixture of normal distributed data
Pattern Recognition Letters
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Analysis
NEC: a hierarchical agglomerative clustering based on fisher and negentropy information
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We introduce a new validity index for crisp clustering that is based on the average normality of the clusters. A normal cluster is optimal in the sense of maximum uncertainty, or minimum structure, and so performing further partitions on it will not reveal additional substructures. To characterize the normality of a cluster we use the negentropy, a standard measure of distance to normality which evaluates the difference between the cluster's entropy and the entropy of a normal distribution with the same covariance matrix. Although the definition of the negentropy involves the differential entropy, we show that it is possible to avoid its explicit computation by considering only negentropy increments with respect to the initial data distribution. The resulting negentropy increment validity index only requires the computation of determinants of covariance matrices. We have applied the index to randomly generated problems, and show that it provides better results than other indices for the assessment of the number of clusters.