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Classification based on Positive and Negative Association Rules
B. Ramasubbareddy, A. Govardhan, A. Ramamohanreddy
Pages - 84 - 92     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 2   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Data Mining, Association Analysis, Classification, Positive and Negative Association Rules
ABSTRACT
Association analysis, classification and clustering are three different techniques in data mining. Associative classification is a classification of a new tuple using association rules. It is a combination of association rule mining and classification. In this, we can search for strong associations between frequent patterns and class labels. The main aim of this paper is to improve accuracy of a classifier. The accuracy can be achieved by producing all types of negative class association rules.
CITED BY (10)  
1 Khan, I. A., & Choi, J. T. (2014). An Application of Educational Data Mining (EDM) Technique for Scholarship Prediction. International Journal of Software Engineering and Its Applications, 8(12), 31-42.
2 Ahmad, A., Shaari, F., & Long, Z. A. (2014, January). Outlier detection method based on hybrid rough: negative using PSO algorithm. In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (p. 108). ACM.
3 Shaari, F., Ahmad, A., & ALong, Z. (2014). Outlier Detection Method based on Hybrid Rough-Negative Algorithm. Journal of Mathematics and System Science, 4(6).
4 Devi, M. K., & Rani, M. U. Fuzzy Weighted Associative Classifier based on Positive and Negative Rules.
5 Kotulla, A. (2012). Negative associacion rules–computing, measures and application areas. Studia Informatica, 33(2B), 273-285.
6 Long, A. (2014) Outlier Detection Method based on Hybrid Rough-Negative Algorithm Mathematics and Systems Science: English, 4 (6), 391-397.
7 Aher, S., & Lobo, L. M. R. J. (2012). Mining association rule in classified data for course recommender system in e-learning. International Journal of Computer Applications, 39(7), 1-7.
8 Aher, S. B., & Lobo, L. M. R. (2012). Best combination of machine learning algorithms for course recommendation system in e-learning. International Journal of Computer Applications, 41(6).
9 B Aher, S., & LMRJ, L. (2012). Combination of clustering, classification & association rule based approach for course recommender system in E-learning. International Journal of Computer Applications, 39(7), 8-15.
10 Kotulla, A. (2012). Negatywne reguly asocjacyjne-wyznaczanie, miary i obszary zastosowania. Studia Informatica, 33(2B), 273-285.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 B.Ramasubbareddy, A.Govardhan, and A.Ramamohanreddy. Adaptive approaches in mining negative association rules. In Intl. conference on ITFRWP-09, India Dec-2009
2 B.Ramasubbareddy, A.Govardhan, and A.Ramamohanreddy. Mining Positive and Negative Association Rules, IEEE ICSE 2010, Hefeai, China, August 2010.
3 R. Agrawal and R. Srikant. Fast algorithms for mining associationrules. In VLDB, Chile, September 1994.
4 J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD, dallas, Texas, 2000.
5 C. Blakeand C. Merz. UCI repository of machine learning databases.
6 S.Brin, R. Motwani, and C.Silverstein. Beyond market baskets: Generalizing association rules to correlations. In ACM SIGMOD, Tucson, Arizona, 1997.
7 D. Thiruvady and G. Webb. Mining negative association rules using grd. In PAKDD, Sydney, Australia, 2004
8 Goethals, B., Zaki, M., eds.: FIMI’03: Workshop on Frequent Itemset Mining Implementations. Volume 90 of CEUR Workshop Proceedings series. (2003) http://CEURWS. org/Vol-90/.
9 Teng, W., Hsieh, M., Chen, M.: On the mining of substitution rules for statistically dependent items. In: Proc. of ICDM. (2002) 442–449
10 Tan, P., Kumar, V.: Interestingness measures for association patterns: A perspective.In: Proc. of Workshop on Postprocessing in Machine Learning and Data Mining. (2000)
11 Gourab Kundu, Md. Monirul Islam, Sirajum Munir, Md. Faizul Bari ACN: An Associative Classifier with Negative Rules 11th IEEE International Conference on Computational Science and Engineering, 2008.
12 Brin,S., Motwani,R. and Silverstein,C., “ Beyond Market Baskets: Generalizing Association Rules to Correlations,” Proc. ACM SIGMOD Conf., pp.265-276, May 1997.
13 Chris Cornelis, peng Yan, Xing Zhang, Guoqing Chen: Mining Positive and Negative Association Rules from Large Databases , IEEE conference 2006.
14 M.L. Antonie and O.R. Za¨?ane, ”Mining Positive and Negative Association Rules: an Approach for Confined Rules”, Proc. Intl. Conf. on Principles and Practice of Knowledge Discovery in Databases, 2004, pp 27–38.
15 Savasere, A., Omiecinski,E., Navathe, S.: Mining for Strong negative associations in a large data base of customer transactions. In: Proc. of ICDE. (1998) 494- 502..
16 Wu, X., Zhang, C., Zhang, S.: efficient mining both positive and negative association rules. ACM Transactions on Information Systems, Vol. 22, No.3, July 2004,Pages 381-405.
17 Wu, X., Zhang, C., Zhang, S.: Mining both positive and negative association rules.In: Proc. of ICML. (2002) 658–665
18 Yuan,X., Buckles, B.,Yuan, Z.,Zhang, J.:Mining Negative Association Rules. In: Proc. of ISCC. (2002) 623-629.
19 Honglei Zhu, Zhigang Xu: An Effective Algorithm for Mining Positive and Negative Association Rules. International Conference on Computer Science and Software Engineering 2008.
20 Pradip Kumar Bala:A Technique for Mining Negative Association Rules . Proceedings of the 2nd Bangalore Annual Compute Conference (2009).
21 Data Mining: Concepts and Techniques Jiawei Han, Micheline Kamber
22 Quinlan, J. 1993 C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann
23 Li, W., Han, J. & Pei, J. 2001 CMAR: Accurate and efficient classification based on multipleclass association rule. In Proceedings of the International Conference on Data Mining (ICDM’01), San Jose, CA, pp. 369–376
24 Dong, G., Zhang, X., Wong, L. & Li, J. 1999 CAEP: Classification by aggregating emerging patterns. In Proceedings of the 2nd Imitational Conference on Discovery Science. Tokyo, Japan: Springer Verlag, pp. 30–42.
25 Antonie, M. & Zaďane, O. 2004 An associative classifier based on positive and negative rules. In Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. Paris, France: ACM Press, pp. 64–69
26 B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD’98), pages 80–86, New York City, NY, August 1998.
27 Thabtah, F., Cowling, P. & Peng, Y. 2004 MMAC: A new multi-class, multi-label associative classification approach. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Brighton, UK, pp. 217–224.
28 Thabtah, F., Cowling, P. & Peng, Y. 2005 MCAR: Multi-class Classification based on Association Rule approach. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications,Cairo, Egypt, pp. 1–7.
29 Yin, X. & Han, J. 2003 CPAR: Classification based on predictive association rule. In Proceedings of the SIAM International Conference on Data Mining. San Francisco, CA: SIAM Press, pp. 369–376. B. Liu, W. Hsu, &Y. Ma, “Integrating classification and association rule mining”, Proceeding of KDD’98, 1998, pp. 80-86.
30 B.Ramasubbareddy, A.Govardhan, A.Ramamohanreddy, An Approach for Mining Positive and Negative Association Rules, Second International Joint Journal Conference in Computer, Electronics and Electrical, CEE 2010
31 B.Ramasubbareddy,A.Govardhan,A.Ramamohanreddy, Mining Indirect Association between Itemsets, proceedings of Intl conference on Advances in Information Technology and Mobile Communication-AIM-2011 published by Springer LNCS , April 21-22, 2011, Nagapur, Maharastra, India
32 B.Ramasubbareddy, A.Govardhan, and A.Ramamohanreddy Mining Indirect Positive and Negative Association Rules, Intl Conference on Advances in Computing and Communications, July 22-24 2011, Kochi, India
Mr. B. Ramasubbareddy
Jyothishmathi Institute of Technology and Science - Afghanistan
rsreddyphd@gmail.com
Dr. A. Govardhan
JNTUH COLLEGE - India
Dr. A. Ramamohanreddy
S.V.University - India