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A Fuzzy Classification Approach to Data Mining

V. Srinivasan, G. Rajenderan, J. Vandar Kuzhali, M. Aruna


Many applications tract to classify data for actionable alerts, which may include for example rule based algorithm, decision tree base algorithm, K-nearest neighbor and so forth. Some classification models are built for their accuracy. We propose a novel way to incorporate FSVM (Fuzzy support vector machine) with a fuzzy approach offers a good comparison between fastness and accuracy. This method can be used for any data set and shows a significant reduction in time to classify the data sets with more accuracy when compared to the SVM. With the fuzzy approach the SVM algorithm shows the high efficiency and good scalability. Our through performance study with some large database and categorical database has shown that FSVM out performs many well-known classifiers in terms of both accuracy and computational efficiency and scales well with regards to the size of the database.


Fizzy Classification, Entropy, Information Gain, SVM.

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