Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
A hybrid self-organizing maps and particle swarm optimization approach: Research Articles
Concurrency and Computation: Practice & Experience - High Performance Computational Biology
Spherical self-organizing map using efficient indexed geodesic data structure
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Particle Swarm Optimisation and Self Organising Maps Based Image Classifier
SMAP '07 Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization
International Journal of Remote Sensing
Spherical and Torus SOM Approaches to Metabolic Syndrome Evaluation
Neural Information Processing
Kohonen-Swarm Algorithm for Unstructured Data in Surface Reconstruction
CGIV '08 Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation
Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map
Expert Systems with Applications: An International Journal
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Investigation of self-organizing map for genetic algorithm
Advances in Engineering Software
Decision of Class Borders on Spherical SOM and Its Visualization
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
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Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test.