Abstract
In this era of big data analysis, mining results hold a very important role. So, the data scientists need to be accurate enough with the tools, methods and procedures while performing rule mining. The major issues faced by these scientists are incremental mining and the huge amount of time that is virtually required to finish the mining task. In this context, we propose a new rule mining algorithm which mines the database in a priority based model for finding interesting relations. In this paper a new mining algorithm using the data structure Treap is explained along with its comparison with the traditional algorithms. The proposed algorithm finishes the task in O (n) in its best case analysis and in O (n log n) in its worst case analysis. The algorithm also considers less frequent high priority attributes for rule creation, thus making sure to create valid mining rules. Thus the major issues of traditional algorithms like creating invalid rules, longer running time and high memory utilization could be remedied by this new proposal. The algorithm was tested against various datasets and the results were evaluated and compared with the traditional algorithm. The results showed a peak performance improvement.
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References
Boney, L., Tewfik, A.H., Hamdy, K.N.: Minimum association rule in large database. In: Proceedings of the Third IEEE International Conference on Computing, pp. 12–16 (2006)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB, pp. 487–499 (1994)
Zaki, M., Parthasarathy, S., Ogihara, M., Li. W.: New algorithms for fast discovery of association rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, vol. 2, pp. 283–296 (1997)
Anandhavalli, G.K.: Association rule mining in genomics. Int. J. Comput. Theory Eng. 1 (2007)
Cooper, C., Zito, M.: Realistic synthetic data for testing association rule mining algorithms for market basket databases. Knowl. Disc. Databases PKDD 9, 398–405 (2007)
Varde, A.S., Takahashi, M., Rundensteiner, E.A., Ward, M.O., Maniruzzaman, M., Sisson, R.D.: Apriori algorithm and game of life for predictive analysis in materials science. Int. J. Knowl. Based Intell. Eng. Syst. 8, 116–122 (2004)
Wu, H., Lu, Z., Pan, L., Xu, R., Jiang, W.: An improved apriori based algorithm for association rules mining. In: Proceedings of Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 51–55 (2009)
Bodon, F.: A fast apriori implementation. In: Proceedings of the IEEE ICDM Workshop on Frequent Item-set Mining Implementation, vol. 9 (2003)
Kryszkiewicz, M., Rybiñski, H.: Data mining in incomplete information systems from rough set perspective. Rough Set Methods Appl. 56, 567–580 (2000)
Kosters, A.W., Marchiori, E., Oerlemans, A.J.: Mining clusters with association rules. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds.) IDA 1999. LNCS, vol. 1642, pp. 39–50. Springer, Heidelberg (1999)
Lin, T.Y.: Rough set theory in very large databases. In: Symposium on Modeling, Analysis and Simulation, vol. 2, pp. 936–941 (1996)
Borgelt, C.: An implementation of FP growth algorithm. In: Proceedings of the Workshop on Open ource Mining Software ACM (2005)
Malik, K., Raheja, N., Garg, P.: Enhance FP growth algorithm. Int. J. Comput. Eng. Manage. 12, 54–56 (2011)
Guy, E.B., Margaret, R.M.: Fast set operations using treaps. In: Proceedings of the Tenth Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 16–26 (1998)
Aragon Cecilia, R., Aragon, C.: Randomized search trees. Algorithmica 16, 464–497 (1996)
Jane, A., Ross, A.: Score normalization in multimodal biometrics systems. Pattern Recogn. 38, 270–285 (2005)
Ben Khalifa, A., Gazzah, S.: Adaptive score normalization: a novel approach for multimodal biometric systems. Int. J. Comput. Electr. Autom. Control Inf. Eng. 7(3) (2013)
Vinodchandra, S.S., Anand, H.S.: Artificial Intelligence and Machine Learning. PHI Publishers, Delhi (2014). 368p. ISBN ISBN: 978-81-203-4934-6
Anand, H.S., Vinodchandra, S.S.: Mining association rules using improved frequent-pattern growth algorithm. Int. J. Appl. Eng. Res. 9, 239–246 (2014). (ISSN: 0973-4562)
Anand, H.S., Vinodchandra, S.S.: Horizontal and vertical rule mining algorithms. In: ACCIS, Proceedings of Elsevier, pp. 26–28 (2014)
Anand, H.S., Vinodchandra, S.S.: Applying correlation threshold on apriori algorithm. In: IEEE ICE-CCN (2013)
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Anand, H.S., Vinodchandra, S.S. (2016). Treap Mining – A Comparison with Traditional Algorithm. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_51
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DOI: https://doi.org/10.1007/978-3-662-49381-6_51
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