Asian Journal of Computer Science and Technology (AJCST)
A New Algorithm for Pattern Based Using Mining Association Rules
Author : S.SelvamVolume 9 No.2 July-December 2020 pp 24-27
Abstract
It is indeed an art to match maximum number of preferences by utilizing limited number of resources. During the current academic year 75% of the admissions to Engineering Colleges have gone down, as only 30% to 40% of intake has been filled. Without reaching the breakeven point, the management of the institution becomes a complicated issue. The main aim of this paper is to discover a pattern to identify the choice of preferences of the candidates to seek admissions in any academic institutions. For the purpose of matching optimum number of candidates to suit our existing system, we have designed our algorithmic approach. Here our new system is used to extract frequent item sets from various preferences. By thresholds, it can fix the preferences either decrease or increase the level of frequent. The new algorithm is based on association rule classification which is one of data mining techniques. Here the frequency of itemset2 is combined with frequency to get itemset3 and continues until item set n. the new algorithm is easy to use and implement because its complexity is less. The application is designed to generate association rule until n-antecedent with one consequent. For this study purpose we have identified 15 most frequently used preferences among the students. The samples we have taken to get association rules are 100 students of Pannai College of Engineering and Technology at Sivagangai. The discovered pattern is common to all institutions. The pattern discovery may be accurate because it is computed by using factors like confidence and support. If this intelligent system is followed strictly, definitely the number of outcomes is increased. The applicant would prefer only when the supply is high. The result of this paper is an application that can generalize association rule among various academic institutions.
Keywords
Mining of Association Rules, Newly developed algorithm, Generating Frequent Item sets, Design Admission Diary, Design a Template.
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References
[1] S.ThabasuKannan, “Optimized mining of Very Large database via ClusteredIndexing Method”, InternationalJournal of Intelligent Optimization Modeling, , Vol 1,pp 3,June 2009.
[2] K.C.C Chan, A.K.C. Wong and D.K.Y. Chiu, “Learning sequential patterns for probabilistic inductive prediction,” IEEE Trans. Systems, Man and Cybernetics, Vol. 24, No. 10, pp. 1532, July 1994.
[3] S.ThabasuKannan, “An algorithmic approach for a simple prototype of business system to get customer satisfaction on CRM”, International Journal of Business Review, Vol 4, pp-26,December2013.
[4] Jian Pei, Jiawei Han, BehzadMortazavi-asl and Hua Zhu, “Mining access patterns efficiently from web logs”, In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 3967,April 2000.
[5] J. M. Kleinberg, R. Kumar, P. Raghavan, S. Rajagopalan and A. S. Tomkins, “The web as a graph: measurements, models and methods”, Lecture Notes in Computer Science, Vol. 162, pp: 18, 1999.
[6] Qiang Yang, Hui Wang and Wei Zhang, “Web-log mining for quantitative temporal event prediction”, IEEE Computational Intelligence Bulletin, Vol.1, No.1, December 2002.
[7] W. Wang and O. R. Zaïane, “Clustering Web Sessions by Sequence Alignment”, Proceedings of DEXA Workshops, pp: 394-398. 2002.
[8] R. Agrawal and R. Srikant. “Fast algorithms for mining association rules in large databases” In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, September 1994.
[9] K.C.C Chan, A.K.C. Wong and D.K.Y. Chiu, “Learning sequential patterns for probabilistic inductive prediction,” IEEE Trans. Systems, Man and Cybernetics, Vol. 24, No. 10, pp. 153, July1994.
[10] Savasere, E. Omiecinski, and S. Navathe. “An efficient algorithm for mining association rules in large databases”. Technical Report GIT-CC-95- 04, Georgia. Institute of Technology, Atlanta. GA 30332, January 1995.