Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition by Serkan Kiranyaz Turker Ince & Moncef Gabbouj

Author:Serkan Kiranyaz, Turker Ince & Moncef Gabbouj
Language: eng
Format: epub
Publisher: Springer Berlin Heidelberg, Berlin, Heidelberg

(6.1)

where is the quantization error (or the average intra-cluster distance) as the Compactness term and is the Separation term, by simply penalizing higher cluster numbers with an exponential, Using the validity index yields the simplest form (i.e., only the nominator of ) and becomes entirely parameter-free.

On the other hand, (hard) clustering has some constraints. Let be the set of data points assigned to a (potential) cluster centroid for a particle a at time t. The clusters should maintain the following constraints:1.Each data point should be assigned to one cluster set, i.e.,

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