A New Approach to Perform Regression Using Minimum Bounding Geometry

Yousef Younes^1, , Jun Sang¹, Ahmad Abdullah² and Ali Baddour¹

¹School of Software Engineering, Chongqing University, Chongqing, P.R. China

²School of Computer Science, Chongqing University, Chongqing, P.R. China

Cite this paper:
Yousef Younes, Jun Sang, Ahmad Abdullah and Ali Baddour. A New Approach to Perform Regression Using Minimum Bounding Geometry. Journal of Computer Sciences and Applications. 2014; 2(2):36-39. doi: 10.12691/jcsa-2-2-4

Abstract

Regression is the data mining process related to estimating a value for a given input by modeling the relationship between the predicators and the response. Choosing the most suitable regression algorithm is the center of big discussion in which the dataset always having the final decision. But when we studied different numerical datasets, we noticed that, data repetition over different intervals is a common property that could be found between any pair of attribute and class values. To exploit this property, this paper begins the journey of finding a new regression approach to address all numeric datasets. The new method uses the minimum bounding geometry to bound the data points in shapes which are used later to suggest values. From the suggested values we choose our targeted prediction value. When we tried this method on different datasets even with the circle as the bounding shape, the results were not perfect but encouraging enough to further elaborate the method. Besides that, the method showed a possibility to do other data mining tasks.

Keywords:
data mining regression minimum bounding geometry classification association rule discovery

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References:

[1]	Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan kaufmann, 2006.
[2]	G. Loterman, and M. Christophe, "Selecting accurate and comprehensible regression algorithms through meta learning." Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on. IEEE, 2012.
[3]	J. R. Rice. "The algorithm selection problem ". Advances in Computers, vol. 15, pp. 65-118, 1976.
[4]	H. Mannila, "Theoretical frameworks for data mining." ACM SIGKDD Explorations Newsletter vol. 1, pp. 30-32, 2000.
[5]	Q. Yang, and X. Wu. "10 challenging problems in data mining research." International Journal of Information Technology & Decision Making vol. 5, pp. 597-604, 2006.
[6]	I. H. Witten, and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
[7]	C. Shah, and A. Jivani. "Comparison of data mining clustering algorithms." Engineering (NUiCONE), 2013 Nirma University International Conference on. IEEE, 2013.
[8]	L. Li, Z. P. Gao, W. Y. Ding, "Fuzzy Multi-class Support Vector Machine Based on Binary Tree in Network Intrusion Detection," Electrical and Control Engineering (ICECE), 2010 International Conference on , vol. , pp. 1043-1046, 2010.
[9]	S. Ullah, Z. Hussain, "A two-step approach for improving efficiency of feedforward Multilayer Perceptrons network," Information and Communication Technologies, ICICT '09. International Conference on, vol. pp. 140-143, 2009.
[10]	G. Holmes, A. Donkin, and I. H. Witten, "WEKA: a machine learning workbench," Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on , vol., no., pp. 357,361, 29 Nov-2 Dec 1994.
[11]	S. Weisberg,” Applied linear regression,” 4th ed., New Jersey, John Wiley & Sons, 2014.
[12]	AJ. Smola, B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, Vol. 14, Issue 3, pp 199-222, Aug 2004.
[13]	L. Wang, “Support Vector Machines: theory and applications,” Vol. 177. Springer, 2005.
[14]	M.Branmer, Principles of Data Mining, Springer, 2013.
[15]	S. Chapman, “MATLAB programming for engineers,” Cengage Learning, 2007.