Paper Info
Title
Clustering of the self-organizing map using particle swarm optimization and validity indices.
Abstract
In this paper, an automatic clustering algorithm applied to self-organizing map (SOM) neurons is presented. The connections of the SOM grid are pruned according to a weighted sum of a set of measures of connection strength between adjacent neurons. The coefficients of the weighted sum are obtained through particle swarm optimization (PSO) search in the multidimensional problem space, where the fitness function is the composed density between and within clusters (CDbw) validity index of strongly connected groups of neurons, while scanning through different values of the minimum cluster size so as to find stable regions with a reasonable trade-off between their length and their mean CDbw value. Simulation results are further presented to show the performance of the proposed method applied to synthetic and real world datasets.
Year
DOI
Venue
2014
10.1109/IJCNN.2014.6889954
IJCNN
Keywords
Field
DocType
self organizing map,particle swarm optimization,indexes,vectors,euclidean distance,clustering algorithms
Particle swarm optimization,Cluster (physics),Correlation clustering,Pattern recognition,Fitness function,Multi-swarm optimization,Artificial intelligence,Cluster analysis,Strongly connected component,Machine learning,Mathematics,Metaheuristic
Conference
ISSN
Citations 
PageRank 
2161-4393
0
0.34
References 
Authors
15
2
Name
Order
Citations
PageRank
Leonardo Enzo Brito da Silva193.31
José Alfredo Ferreira Costa2102.32