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Volume 4 | Issue 4 | Year 2013 | Article Id. IJCTT-V4I4P155 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I4P155
Association Technique in Data Mining and Its Applications
Harveen Buttar, Rajneet Kaur
Citation :
Harveen Buttar, Rajneet Kaur, "Association Technique in Data Mining and Its Applications," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 4, pp. 715-719, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I4P155
Abstract
Data mining provides us with a variety of techniques for pattern analysis on large data such as association, clustering, segmentation and classification for better manipulation of data. This paper presents that how the data mining technique : association can be used in different areas. For instance, this technique helps the pharma firms to compete on lower costs while improving the quality of drug discovery and delivery methods. Also, this technique can be helpful in breast cancer diagnosis and prognosis. It shows that association rule mining algorithms can be used in the classification approach.
Keywords
Data Mining, Assocation, Drug Discovery, Breast Cancer.
References
[1] Liu, B., Hsu, W., and Ma, Y. (1998). Integrating Classification and association rule mining. In KDD ’98, New York, NY, Aug. 1998.
[2] Thabtah, F., Cowling, P. and Peng, Y. H. (2004). MMAC: A New Multi-Class, Multi-Label Associative Classification Approach. Fourth IEEE International Conference on Data Mining (ICDM'04).
[3] Li, W., Han, J., and Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple-class association rule. In ICDM’01, pp. 369-376, San Jose, CA.
[4] Agrawal, R., Amielinski, T., and Swami, A. (1993). Mining association rule between sets of items in large databases. In Proceeding of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216, Washington, DC, May 26-28.
[5] Shweta Kharya," Using Data Mining Techniques for Diagnosis and Prognosis of Cancer Disease" , International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
[6] Maria-Luiza Antonie, Osmar R. Za¨ıane, Alexandru Coma, .Application of Data Mining Techniques for Medical Image Classification.Proceeding of second International worshop on Mutimedia data mining(MDM/KDD’2001),in conjuction with ACM SIGKDD conference.SAN FRANCISCO,USA,AUG 26,2001
[7] Journal of Theoretical and Applied Information Technology (2005), Applications of Data Mining Techniques in Pharmaceutical Industry.
[8] Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rule. Proceedings of the 20th International Conference on Very Large Data Bases. pp. 487 - 499.
[9] Hipp, J., Güntzer, U., and Nakhaeizadeh, G. (2000). Algorithms for association rule mining — a general survey and comparison. SIGKDD Explor. Newsl. 2, 1 (Jun. http://doi.acm.org/10.1145/360402.360421 2000), 58-64. DOI=
[10] Li, J., Zhang, X., Dong, G., Ramamohanaro, K. and Sun, Q. (1999). Efficient mining of high confidence rules without support thresholds. A book chapter in the book “Principles of Data Mining and Knowledge Discovery”. LNCS.
[11] Park, J. S., Chen, M., and Yu, P. S. (1995). An effective hash-based algorithm for mining association rules. SIGMOD Rec. 24, 2 (May.1995), 175-186. DOI= http://doi.acm.org/10.1145/568271.223813
[12] Blackmore, K. and Bossomaier, T. J. (2003). Comparison of See5 and J48.PART Algorithms for Missing Persons Profiling. Technical report. Charles Sturt University, Australia.
[13] Han, J., Pei, J., and Yin, Y. (2000) Mining frequent patterns without candidate generation.. 2000 ACM SIGMOD Intl. Conference on Management of Data.
[14] Dong, G., Li, J. (1999). Efficient mining of frequent patterns: Discovering trends and differences. In Proceeding of SIGKDD 1999, San Diego, California.
[15] Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rule. Proceedings of the 20th International Conference on Very Large Data Bases. pp. 487 - 499.