Self-organizing maps
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Generalized part family formation through fuzzy self-organizing feature map neural network
Computers and Industrial Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organizing Maps
The Visualisation Capability of Self-Organizing Maps to Detect Deviations in Distribution Control
The Visualisation Capability of Self-Organizing Maps to Detect Deviations in Distribution Control
Computers in Industry - Special issue: Soft computing in industrial applications
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Soft computing system for bank performance prediction
Applied Soft Computing
On the use of self-organizing maps for clustering and visualization
Intelligent Data Analysis
Computers in Biology and Medicine
CLASS: An algorithm for cellular manufacturing system and layout design using sequence data
Robotics and Computer-Integrated Manufacturing
Exploring Topology Preservation of SOMs with a Graph Based Visualization
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Application of self-organizing maps in analysis of wave soldering process
Expert Systems with Applications: An International Journal
A modelling and optimization system for fluidized bed power plants
Expert Systems with Applications: An International Journal
Manufacturing cell formation with production data using neural networks
Computers and Industrial Engineering
Self-organizing map network as an interactive clustering tool - An application to group technology
Decision Support Systems
Engineering Applications of Artificial Intelligence
Self-organizing feature map for cluster analysis in multi-disease diagnosis
Expert Systems with Applications: An International Journal
Quality-oriented optimization of wave soldering process by using self-organizing maps
Applied Soft Computing
A novel self-organizing map (SOM) neural network for discrete groups of data clustering
Applied Soft Computing
A linear assignment clustering algorithm based on the least similar cluster representatives
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Sense of Touch in Robots With Self-Organizing Maps
IEEE Transactions on Robotics
Computers and Industrial Engineering
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Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, work-in-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using self organizing map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.