Title: Visual interactive evolutionary algorithm for high dimensional outlier detection and data clustering problems
Authors: Lydia Boudjeloud-Assala
Addresses: LITA, Université Paul Verlaine Metz, Ile du Saulcy, 57045 Metz Cedex 01, France
Abstract: Usual visualisation techniques for multidimensional datasets, such as parallel coordinates and scatterplot 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 visualisation tools: the evolutionary algorithm is used to obtain optimal dimension subsets which represent the original dataset without loosing information for unsupervised mode (clustering or outlier detection). The last effective cooperation is a visualisation 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 dataset to confirm this approach is effective for supporting the user in the exploration of high dimensional datasets.
Keywords: evolutionary algorithms; EAs; visual interactive algorithms; high dimensionality; data clustering; outlier detection; bio-inspired computation; visualisation; automatic algorithms; high dimensional datasets.
International Journal of Bio-Inspired Computation, 2012 Vol.4 No.1, pp.6 - 13
Available online: 16 Jan 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article