Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Towards effective and interpretable data mining by visual interaction
ACM SIGKDD Explorations Newsletter
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Guest Editor's Introduction: Visual Data Mining
IEEE Computer Graphics and Applications
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Visualization of multi-algorithm clustering for better economic decisions - The case of car pricing
Decision Support Systems
An Outlier Detection Algorithm Based on Arbitrary Shape Clustering
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Usual visualization techniques for multidimensional data sets, such as parallel coordinates and scatter-plot matrices, do not scale well to high numbers of dimensions. A common approach to solve this problem is dimensionality selection. Existing dimensionality selection techniques usually select pertinent dimension subsets that are significant to the user without loose of information. We present concrete cooperation between automatic algorithms, interactive algorithms and visualization tools: the evolutionary algorithm is used to obtain optimal dimension subsets which represent the original data set without loosing information for unsupervised mode (clustering or outlier detection). The last effective cooperation is a visualization tool used to present the user interactive evolutionary algorithm results and let him actively participate in evolutionary algorithm searching with more efficiency resulting in a faster evolutionary algorithm convergence. We have implemented our approach and applied it to real data set to confirm this approach is effective for supporting the user in the exploration of high dimensional data sets.