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Fuzzy Classification to Classify the Income Category Based On Entropy
Keywords:
Classification, Entropy, Information Gain, ID3, Decision Tree, FuzzyAbstract
The classification problem is one of the main issues in data mining because it aims to extract a classifier which can be used to predict the classes of objects whose class table are unknown. This paper deals with classifying the income database with the entropy based method for analyzing the income is high or low. This method incorporates two mathematical techniques Entropy and Information Gain (IG) with Interactive Dichotomize 3 Algorithm (ID3). Subsets are calculated through Entropy. We fix the threshold point based on the fuzzy approach and the factors are identified using IG. The ID3 algorithm is used to derive a decision tree which classifies the income. This method also helps to extract logical rules that could be used in classifying high or low based on income with various attributed.
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References
[1] J. and Kamber M., Data Mining Concepts and Techniques, Morgan Kaufmann Publishers,2000.
[2] K. Alsabti, S. Ranka, and V. Singh, CLOUDS: A Decision Tree Classifier for Large Datasets, Proc. Fourth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’98), 1998.
[3] X. Yin and J. Han, CPAR: Classification based on Predictive Association Rules, Proc. Third SIAM Int’l Conf. Data Mining, 2003.
[4] J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publisher Inc., 1993.
[5] J.C. Fodor, On Fuzzy Implication, Fuzzy sets and systems, vol. 42, pp. 293-300, 1991.
[6] C.J. Mertz and P.M. Murphy, UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/pub/machinelearining-databases, 2008
[7] L. Breslow and D. Aha, Simplifying Decision Trees, Knowledge Eng. Rev., vol. 12, no. 1, pp. 1-40, 1997.
[8] L.X. Wang. Adaptive Fuzzy Systems and Control. PTR Prentice Hall, 1994.
[9] D. Dubois and H. Prade, Rough Fuzzy Sets and Fuzzy Rough Sets, Int. J. General Systems, vol. 17, nos. 2-3, pp. 191-209,1990
[10] S. Greco, M. Inuiguchi, and R. Showinski, Fuzzy Rough Sets and Multiple-premise Gradual Decision Rules, Inr.J. Approximate Reasoning, vol. , no. , pp. 179-211, 2006.
[11] Andrew Colin, Building Decision Trees with ID3 Algorithm. Dr. Dobbs Journal, June 1996.
[12] Z. Pawlak, Rough Sets, Decision Algorithms and Bayes Theorem, European J. Operational Research, vol.136, pp.-2002
[13] H. Wang and C. Zaniolo, CMP: A Fast Decision Tree Classifier Using Multivariate Predications, Proc. 17th Int’l Conf. Data Eng. (ICDE ’01), 2001.
[2] K. Alsabti, S. Ranka, and V. Singh, CLOUDS: A Decision Tree Classifier for Large Datasets, Proc. Fourth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’98), 1998.
[3] X. Yin and J. Han, CPAR: Classification based on Predictive Association Rules, Proc. Third SIAM Int’l Conf. Data Mining, 2003.
[4] J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publisher Inc., 1993.
[5] J.C. Fodor, On Fuzzy Implication, Fuzzy sets and systems, vol. 42, pp. 293-300, 1991.
[6] C.J. Mertz and P.M. Murphy, UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/pub/machinelearining-databases, 2008
[7] L. Breslow and D. Aha, Simplifying Decision Trees, Knowledge Eng. Rev., vol. 12, no. 1, pp. 1-40, 1997.
[8] L.X. Wang. Adaptive Fuzzy Systems and Control. PTR Prentice Hall, 1994.
[9] D. Dubois and H. Prade, Rough Fuzzy Sets and Fuzzy Rough Sets, Int. J. General Systems, vol. 17, nos. 2-3, pp. 191-209,1990
[10] S. Greco, M. Inuiguchi, and R. Showinski, Fuzzy Rough Sets and Multiple-premise Gradual Decision Rules, Inr.J. Approximate Reasoning, vol. , no. , pp. 179-211, 2006.
[11] Andrew Colin, Building Decision Trees with ID3 Algorithm. Dr. Dobbs Journal, June 1996.
[12] Z. Pawlak, Rough Sets, Decision Algorithms and Bayes Theorem, European J. Operational Research, vol.136, pp.-2002
[13] H. Wang and C. Zaniolo, CMP: A Fast Decision Tree Classifier Using Multivariate Predications, Proc. 17th Int’l Conf. Data Eng. (ICDE ’01), 2001.
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Published
2011-10-03
How to Cite
Srinivasan, V., Govind, R., Jagannathan, V. K., & Murugesan, A. (2011). Fuzzy Classification to Classify the Income Category Based On Entropy. Journal of Computer Science and Technology, 11(02), p. 81–85. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/672
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