Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neurocomputing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Unsupervised Rough Set Classification Using GAs
Journal of Intelligent Information Systems
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
Clustering Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computer Algorithms: Introduction to Design and Analysis
Computer Algorithms: Introduction to Design and Analysis
Genetic Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Web Intelligence and Agent Systems
Fuzzy C-means clustering of web users for educational sites
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Comparison of conventional and rough K-means clustering
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Web Intelligence and Agent Systems
A new grouping genetic algorithm for clustering problems
Expert Systems with Applications: An International Journal
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Modern day computers cannot provide optimal solution to the clustering problem. There are many clustering algorithms that attempt to provide an approximation of the optimal solution. These clustering techniques can be broadly classified into two categories. The techniques from first category directly assign objects to clusters and then analyze the resulting clusters. The methods from second category adjust representations of clusters and then determine the object assignments. In terms of disciplines, these techniques can be classified as statistical, genetic algorithms based, and neural network based. This paper reports the results of experiments comparing five different approaches: hierarchical grouping, object-based genetic algorithms, cluster-based genetic algorithms, Kohonen neural networks, and K-means method. The comparisons consist of the time requirements and within-group errors. The theoretical analyses were tested for clustering of highway sections and supermarket customers. All the techniques were applied to clustering of highway sections. The hierarchical grouping and genetic algorithms approaches were computationally infeasible for clustering a larger set of supermarket customers. Hence only Kohonen neural networks and K-means techniques were applied to the second set to confirm some of the results from previous experiments.