JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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CONCLUSION Yuhanis Yusof and Mohammed Hayel Refai In this paper, a modified Multi-class classification based on association rule mining is presented. The so-called MMCAR algorithm includes a new item production algorithm in generating relevant association rules. A more restricted condition is included in the item production so that MMCAR only selects the best related items in producing association rules of the classifier. Hence, a moderate size of association rules will then be obtained. Such a result will aid users in interpreting and understanding the nature of data in the context of study. The proposed MMCAR is proven to be at par with MCAR in prediction accuracy but outperforms the benchmark method in producing lesser number of association rules. REFERENCES Baralis, E., Chiusano, S., & Garza, P. (2004). On support thresholds in associative classification. Proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus, pp. 553-558. Baralis, E., Chiusano, S., & Garza, P. (2008). A lazy approach to associative classification. IEEE Transactions on Knowledge and Data Engineering, 20, 156-171. Cohen, W. W. (1995). Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, pp. 115-123. Li, W., Han, L., & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple classassociation rules. In Proceedings of the ICDM’01 San Jose, CA, p. 369. Lim, T. S., Loh, W. Y., & Shih, Y. S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine learning, 40, 203-228. Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. Knowledge discovery and data mining, 80–86. Meretakis, D., & Wüthrich, B. (1999). Extending naïve Bayes classifiers using long item sets. In Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, pp. 165-174. Merz, C., & Murphy, P. (1996). UCI repository of machine learning databases. Retrieved from FTP of ics. uci.edu in the directory pub/machine-learning-databases. Tang, Z., & Liao, Q. (1998). A new class based associative classification algorithm. IAENG International Journal of Applied Mathematics, 36(2), 136-141. Thabtah, F., Cowling, P., & Peng, Y. (2005). MCAR: multi-class classification based on association rule. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications, p. 33. Thabtah, F., Crowling, P., & Hammoud, S. (2006). Improving rule sorting, predictive accuracy and training time in associative classification. Expert Systems with Applications, 31, 414-426. Thabtah, F. A., & Cowling, P. I. (2007). A greedy classification algorithm based on association rule. Applied Soft Computing, 7, 1102-1111. Thabtah F., Mahmood Q., McCluskey L., & Abdel-jaber H. (2010). A new Classification based on Association Algorithm. Journal of Information and Knowledge Management, 9(1), 55-64. Quinlan, J. R. (1993). C4.5: Programs For Machine Learning. Morgan Kaufmann. 214 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
Association Rule Mining Quinlan, J. R. (1998). Data mining tools See5 and C5.0. Technical Report, RuleQuest Research. Wang, K., He, Y., & Cheung, D. W. (2001). Mining confident rules without support requirement. In Proceedings of the Tenth International Conference on Information and Knowledge Management. Atlanta, Georgia, pp. 89-96. WEKA (2000). Data Mining Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ml/weka. Yin, X., & Han, J. (2003). CPAR: Classification based on predictive association rules. Paper presented at the 2003 SIAM International Conference on Data Mining, Cathedral Hill Hotel, San Francisco, CA, May 1-3, 2003. pp. 331. Yoon, Y., & Lee, G. G. (2008). Text Categorization Based on Boosting Association Rules. Paper presented at the the 2008 IEEE International Conference on Semantic Computing. pp.136-143. Zaki, M. J., & Gouda, K. (2003). Fast vertical mining using diffsets. Paper presented at the 9 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, D.C., pp. 326-335. Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 215
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Journal of Science & Technology Jou
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Selected Articles from UPM-Malaysia
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ISSN: 0128-7680 © 2013 Universiti
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Aspect of Fatigue Analysis of Compo
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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Statistical Analysis Norhashila Has
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INTRODUCTION Tajau, R. et al. A hig
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Aji, I. S., Zinudin, E. S., Khairul
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Aji, I. S., Zinudin, E. S., Khairul
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CONCLUSION Aji, I. S., Zinudin, E.
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REFERENCES N. Maizatul, I. Norazowa
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The Role of Similarity Measurement
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TABLE 5: Winners MRS The Role of Si
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REFEREES FOR THE PERTANIKA JOURNAL
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Guidelines for Authors Publication
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table should be prepared on a separ
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Usability of Educational Computer G
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