An overview of mapping techniques for exploratory pattern analysis
Pattern Recognition
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Self-organizing maps
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Some studies on fuzzy clustering of psychosis data
International Journal of Business Intelligence and Data Mining
Developing fuzzy classifiers to predict the chance of occurrence of adult psychoses
Knowledge-Based Systems
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A human being can visualise only up to 3-dimensions. A mapping tool is essential to map the higher dimensional data to a lower dimension for visualisation. Both linear as well as non-linear mapping methods have been used by various researchers for the said purpose. In the present work, a non-linear mapping method based on a genetic algorithm has been developed and its performance is compared to that of other methods, namely Sammon's NLM, VISOR and SOM, in terms of accuracy in mapping, visibility of the mapped data and computational complexity, for solving Schaffer's and DeJong's test functions. The proposed GA-like approach and VISOR algorithm are found to be the best and worst, respectively, in terms of accuracy in mapping, ease of visualization. Moreover, the GA-like approach and VISOR algorithm are seen to be the slowest and fastest, respectively, of all the methods.