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Analysis of Data Mining Visualization Techniques Using ICA AND SOM Concepts

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Author(s): K. S. Rathnamala | R. S. D. Wahida Banu

Journal: International Journal of Computer Science and Information Security
ISSN 1947-5500

Volume: 9;
Issue: 1;
Start page: 171;
Date: 2011;
Original page

Keywords: Independent component analysis | Self organizing map | Vector quantization | patterns | Agglomerative hierarchical methods | Time series segmentation | Finding patterns by proximity | Clustering validity indices | Feature selection and weighing Fast ICA.

ABSTRACT
This research paper is about data mining (DM) and visualization methods using independent component analysis and self organizing map for gaining insight into multidimensional data. A new method is presented for an interactive visualization of cluster structures in a self-organizing Map. By using a contraction model, the regular grid of selforganizing map visualization is smoothly changed toward a presentation that shows better the proximities in the data space. A Novel Visual Data Mining method is proposed for investigating the reliability of estimates resulting from a Stochastic independent component analysis (ICA) algorithm. There are two algorithms presented in this paper that can be used in a general context. Fast ICA for independent binary sources is described. The model resembles the ordinary ICA model but the summation is replaced by the Boolean Operator OR and the multiplication by AND. A heuristic method for estimating the binary mixing matrix is also proposed. Furthermore, the differences on the results when using different objective function in the FastICA estimation algorithm is also discussed.
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