{"title":"Post Mining- Discovering Valid Rules from Different Sized Data Sources","authors":"R. Nedunchezhian, K. Anbumani","volume":7,"journal":"International Journal of Computer and Information Engineering","pagesStart":2096,"pagesEnd":2103,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10650","abstract":"
A big organization may have multiple branches spread across different locations. Processing of data from these branches becomes a huge task when innumerable transactions take place. Also, branches may be reluctant to forward their data for centralized processing but are ready to pass their association rules. Local mining may also generate a large amount of rules. Further, it is not practically possible for all local data sources to be of the same size. A model is proposed for discovering valid rules from different sized data sources where the valid rules are high weighted rules. These rules can be obtained from the high frequency rules generated from each of the data sources. A data source selection procedure is considered in order to efficiently synthesize rules. Support Equalization is another method proposed which focuses on eliminating low frequency rules at the local sites itself thus reducing the rules by a significant amount.<\/p>\r\n","references":"[1] Agarwal, R. and Srikant, R,\u00d4\u00c7\u00ffFast Algorithms for Mining Association\r\nRules, Proc. Very Large Database Conf. 1994.\r\n[2] R.Agarwal. T.Imielinski and A. Swami, Mining Association Rules\r\nbetween Sets of Items in Large Databases, Proc. ACM International\r\nConferences on Management of Data, 1993, pp.207-216.\r\n[3] Cheung, D. Lee, S. and Kao, B., Maintenance of Discovered\r\nAssociation Rules in Large Databases: An Incremental Updating\r\nTechnique, Proc. 12th Int-l Conf. Data Eng., 1996, pp. 106-114.\r\n[4] U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy,\r\nAdvances in Knowledge Discovery and Data Mining. AAAI Press\/The\r\nMIT Press, 1996.\r\n[5] Han, J. Pei, J. and Yin, Y. , Mining Frequent Patterns Without Candidate\r\nGeneration, Proc. ACM SIGMOD Int-l Conf. Management of Data,\r\n2000, pp. 1-12.\r\n[6] Jia-Wei Han and Micheline Kamber (2001), Data Mining: Concepts and\r\nTechniques, Morgan Kaufmann Publishers.\r\n[7] R.Nedunchezhian and K.Anbumani, Single Scan Frequent set\r\nGeneration in Association Rule Mining, Proc. 1st International\r\nComputer Engineering Conference New Technologies for the\r\nInformation Society, Cairo University, Egypt, 2004, 300-305.\r\n[8] Park, J.S. Chen, M.S. and Yu, P.S., An Effective Hash Based Algorithm\r\nfor Mining Association Rules, Proc. ACM SIGMOD Conf. Management\r\nof Data, 1995.\r\n[9] Rastogi, R. and Shim, K., Mining Optimized Support Rules for Numeric\r\nAttributes, Proc. ACM SIGMOD Conf. Management of Data, 1999.\r\n[10] Simovici, Dan A. Cristofor, Laurentiu and Cristofor, Dana, Galois\r\nConnections and Data mining, J.UCS: Journal of Universal Computer\r\nScience, 2000.\r\n[11] Webb, G.I., Efficient Search for Association Rules, Proc. ACM\r\nSIGKDD Int-l Conf. Knowledge Discovery and Data Mining, 2000,\r\npp. 99-107.\r\n[12] Wu, Xindong and Zhang, Shichao, Synthesizing High- Frequency Rules\r\nfrom Different Data Sources, IEEE Trans. Knowledge and Data Eng.,\r\nvol. 15, no.2., Mar\/Apr 2003.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 7, 2007"}