Open Access Open Access  Restricted Access Subscription or Fee Access

A Fuzzy Classification Approach to Data Mining

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

Abstract


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.

Keywords


Fizzy Classification, Entropy, Information Gain, SVM.

Full Text:

PDF

References


M.E.Mavroforakis and S.Theodoridis, “A Geometric Approach to Support Vector achine(SVM) Classification”, IEEE Trans. Neural Networks, Vol.17, No.3, 671-682, 2006.

Jiawei Han, Micheline Kamber , “Data Mining: Concepts and Techniques, 2nd ed.,” Morgan Kaufmann Publishers, March. 2006. ISBN 1-55860-901-6

H.Yu , J.Yang and J.Han , “Classifying Large Data Sets Using SVMs with Hierarchical Clusters”, Proc. of the 9th ACM SIGKDD 2003, Washington, DC, USA, 2003.

J-X.Dong, A.Krzyzak, and C.Y.Suen, “Fast SVM Training Algorithm with Decomposition on Very Large Data Sets”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.27, no.4, pp.603-618, 2005

J-X. Dong, A. Krzyzak, and C.Y. Suen, “Fast SVM Training Algorithm with Decomposition on Very Large Data Sets”, IEEE Trans. Pattern Analysis and Machine Intelligence, 27(4), 2005, pp.603-618.

Liping Jing, Michael K. Ng, and Joshua Zhexue Huang. “An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data”. IEEETrans.Knowledge and Data Engineering, Vol.19, No.8, 2007, pp. 1026-1041.

E. Halperin and R. M. Karp, “The minimum-entropy set cover problem,” Theor. Comput. Sci., vol. 348, no. 2, pp. 240–250, Dec. 2005.

Lee, S.U., Chung, S.Y., and Park, R.H, “A comparative performance study of several global thresholding techniques for segmentation”, Comput. Vis. Graph. Image Process., 1990, 52, pp. 171–190

Lee, S.S., Horng, S.-J., and Tsai, H.-R, “Entropy thresholding and its parallel algorithm on the reconfigurable array of processors with wide bus networks”, IEEE Trans. Image Process., 1999, 8, (9), pp. 1229–1242

Xue-Ming Leng, Yi- Ding Wang, “Gender Classification Based on Fuzzy SVM”, Proc. of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July 2008

H. P. Huang and Y. H. Liu, “Fuzzy support vector machines for pattern recognition and data mining,” Int. J. Fuzzy Syst., vol. 4, pp. 826–835, Sep. 2002.

C.F. Lin, S.D. Wang, “Fuzzy support vector machines,” IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 464-471, Mar. 2002.

Eric C.C Tsang, Daniel S. Yeung, Patrick P.K. Chan, “Fuzzy Support Vector Machines for Solving Two-Class Problems” Proc. of the Second International Conference on Machine Learning and Cybernetics, Wan, 2-5 November 2003

Yixin Chen,and James Z. Wang, “Support Vector Learning for Fuzzy Rule-Based Classification Systems”, IEEE Transactions on Fuzzy Systems Vol.11, No.6, December 2003.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.