The Community for Technology Leaders
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
ISBN: 978-0-7695-3507-4
pp: 713-717
Data mining is a process to discover useful, possibly unexpected, patterns from the large set of data and widely used in the large information processing applications. Classification is used to classify the data into a set of classes based on some attributes for further processing. Real world application contains very large,imprecise and noisy data. In this case, the knowledge representation need some linguistic term instead of discrete value. In such a scenario fuzzy logic based processing is a natural choice. In this paper, we propose an algorithm for classification using fuzzy approach,which performs class dependent fuzzy discretization.The continuous numeric data is converted in linguistic form which helps in fuzzy classification. The proposed method is compared with standard non-fuzzy discretization techniques where class-attribute relationship is not considered and intervals are predefined. Simulation results show the improved performance of the proposed method.
Data Mining (DM), Fuzzy based Classification, Interval discretization, Fuzzy discretization

R. G. Mehta, M. A. Zaveri and D. P. Rana, "A Novel Fuzzy Based Classification for Data Mining Using Fuzzy Discretization," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 713-717.
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