Research Article Open Access

A Fuzzy Fast Classification for Share Market Database with Lower and Upper Bounds

Srinivasan Vaiyapuri1, Rajenderan Govind2 and Vandar Kuzhali Jaganathan1
  • 1 Department of Computer Applications, Velalar College of Engineering and Technology, India
  • 2 Department of Science and Humanities, Kongu Engineering College, Erode, Tamilnadu-638012, India


In recent years, many researchers focused on the research topic of constructing fuzzy classification system. This study introduces a Fuzzy Fast Classification (FFC) approach for large data sets. It has three phases, in the first phase the large data base is reduced with the entropy by removing the number of attribute. In the second phase an approximate classification is obtained by the mean separation of the data by the total weight, upper and lower approximation line is drawn such that 20% of the record lies near the mean line. In the third phase the classification is refined by using fuzzy logic approach for the 20% of the record since they may fall in any one of the category which need to be carefully examined with the degree of fuzzy value. Experimental results for share market database demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. The proposed classifier has distinctive advantages on dealing with huge data sets.

American Journal of Applied Sciences
Volume 9 No. 12, 2012, 1934-1939


Submitted On: 9 July 2012 Published On: 8 November 2012

How to Cite: Vaiyapuri, S., Govind, R. & Jaganathan, V. K. (2012). A Fuzzy Fast Classification for Share Market Database with Lower and Upper Bounds. American Journal of Applied Sciences, 9(12), 1934-1939.

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  • Classification
  • Entropy
  • Information Gain
  • SVM
  • Fuzzy SVM
  • Fuzzy Fast Classification