Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map

Authors:
Akın Özçift;Mehmet Kaya;Arif Gülten;Mustafa Karabulut
Affiliations:
Department of Electrical and Electronics Engineering, Firat University, Turkey;Department of Computer Engineering, Firat University, Turkey;Department of Electrical and Electronics Engineering, Firat University, Turkey;Department of Electrical and Electronics Engineering, Cukurova University, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 5
Cited 5

Combinatorial Approaches to Finding Subtle Signals in DNA Sequences

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization

IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins

Bioinformatics
A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set

Pattern Recognition Letters
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm

Pattern Recognition Letters

Parallel computation models of particle swarm optimization implemented by multiple threads

Expert Systems with Applications: An International Journal
Improved particle swarm optimization to minimize periodic preventive maintenance cost for series-parallel systems

Expert Systems with Applications: An International Journal
A review on particle swarm optimization algorithms and their applications to data clustering

Artificial Intelligence Review
Pattern classification and audiovisual content management techniques using hybrid expert systems: A video-assisted bioacoustics application in Abdominal Sounds pattern analysis

Expert Systems with Applications: An International Journal
Multistrategy self-organizing map learning for classification problems

Computational Intelligence and Neuroscience

Quantified Score

Hi-index	12.07

Visualization

Abstract

This work studies the optimization of SOM algorithm in terms of reducing its training time by the use of a swarm intelligence method, i.e. particle swarm optimization (PSO). Our novel algorithm optimizes SOM with PSO and reduces computational time of the training phase of SOM significantly. The performance of the algorithms has been tested with genomic datasets, biomedical datasets and an artificial dataset to show the efficiency of swarm optimized SOM, i.e. SWOM. The experimental comparison between SOM and SWOM, has demonstrated significant reduction in training time of SWOM with preservation of clustering quality.