Abstract
Interactive visual clustering allows the user to be involved into the clustering through visualizing process via interactive visualization. In order to perform effective interaction in the visual clustering process, the efficient feature selection methods are required to identify the most dominating features. Hence, in this paper an improved visual clustering system is proposed using an efficient feature selection method. The relevant features for visual clustering are identified based on their contribution to the entropy. Experimental results show that the proposed method works well in finding the best cluster.
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References
Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering 16(11) (2004)
Dy, J.G., Broadly, E.C.: Feature Selection for unsupervised learning. J. Machine Learning Research 5, 845–889 (2004)
Guha., G., Rastogi., R., Shim, K.: CURE: An efficient clustering algorithm for large databases. In: Proc. of the ACM SIGMOD, pp. 73–84 (1998)
Hinnerburg, A., Keim, D., Wawryniuk, M.: HD-Eye:Visual Mining of High – Dimensional Data. IEEE Computer Graphics and Applications 19(5), 22–31 (1999)
Kandogan, E.: Visualizing Multi-dimensional Clusters. Trends and outliers using star co-ordinates. In: Proc. of ACM KDD, pp. 107–116 (2001)
Chen, K., Liu, L.: VISTA: Validating and Refining Clusters via Visualization. Information Visualization 4, 257–270 (2004)
Chen, K., Liu, L.: iVIBRATE: Interactive Visualization-Based Framework for Clustering Large Datasets. ACM Transactions on Information Systems 24, 245–294 (2006)
desJardins, M., MacGlashan, J., Ferraioli, J.: Interactive visual clustering. Intelligent User Interfaces, 361–364 (2007)
Tory, M., Moller, T.: Human Factors in Visualization Research. IEEE Transactions on Visualization and Computer Graphics 10(1) (2004)
Mithra, P., Murthy, C.A., Pal, S.K.: Unsupervised Feature Selection using Feature Similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 301–312 (2002)
Jouve, P.-E., Nicoloyannis, N.: A filter feature selection method for clustering, pp. 583–593. Springer, ISMIS, Heidelberg (2005)
Vayshavsky, R., Gottlieb, A., Linial, M., Horn, D.: Noval Unsupervised Feature Filtering of Biological Data. In: Text Mining and Information extraction, pp. 1–7. Oxford University Press, Oxford (2004)
Thangavel, A.P.: Feature selection for Medical database using Rough System. International Journal on Artificial Intelligence and Machine Learning 6(1), 11–17 (2006)
Wall, M.E., Rechtsteiner, A., Rocha, L.M.: Singular value Decomposition and Principal component Analysis. In: Berrar, D., Dubitzky (eds.) A Practical approach to Microarray data analysisi, pp. 91–109. Kluwer, Dordrecht
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Thangavel, K., Alagambigai, P., Devakumari, D. (2009). Improved Visual Clustering through Unsupervised Dimensionality Reduction. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_53
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DOI: https://doi.org/10.1007/978-3-642-10646-0_53
Publisher Name: Springer, Berlin, Heidelberg
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