Abstract
Pattern mining in large databases is the fundamental and a non-trivial task in data mining. Most of the current research focuses on frequently occurring patterns, even though less frequently/rarely occurring patterns benefit us with useful information in many real-time applications (e.g., in medical diagnosis, genetics). In this paper, we propose a novel algorithm for mining rare itemsets using recursive elimination (RELIM)-based method. Simulation results indicate that our approach performs efficiently than existing solution in time taken to mine the rare itemsets.
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References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Los Altos (2000)
Agarwal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, pp. 487–499. 12–15 September 1994, Morgan Kaufmann, Santiago de Chile, Chile (1994)
Szathmary, L., Valtchev, P., Napoli, A., Godin, R.: Efficient vertical mining of minimal rare itemsets. In: CLA, pp. 269–280. Citeseer (2012)
Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. In: 19th IEEE International Conference on Tools with Artificial Intelligence, 2007, ICTAI 2007, vol. 1, pp. 305–312. IEEE (2007)
Borgelt, C.: Keeping things simple: finding frequent item sets by recursive elimination. In: Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp. 66–70. ACM (2005)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)
Goethals, B.: Survey on frequent pattern mining (2003)
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent patterns with counting inference. ACM SIGKDD Explor. Newsl 2(2), 66–75 (2000)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Disc. 15(1), 55–86 (2007)
Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explor. Newsl 6(1), 7–19 (2004)
Koh, Y.S., Rountree, N.: Finding sporadic rules using apriori-inverse. In: Advances in Knowledge Discovery and Data Mining, pp. 97–106. Springer (2005)
Troiano, L., Scibelli, G., Birtolo, C.: A fast algorithm for mining rare itemsets. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications, pp. 1149–1155. IEEE (2009)
Dataset, F.: Frequent itemset mining implementation (fimi) dataset
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Kataria, M., Oswald, C., Sivaselvan, B. (2019). A Novel Rare Itemset Mining Algorithm Based on Recursive Elimination. In: Hoda, M., Chauhan, N., Quadri, S., Srivastava, P. (eds) Software Engineering. Advances in Intelligent Systems and Computing, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-10-8848-3_22
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DOI: https://doi.org/10.1007/978-981-10-8848-3_22
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