Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Incremental Mining of Shocking Association Patterns

Authors: Eiad Yafi, Ahmed Sultan Al-Hegami, M. A. Alam, Ranjit Biswas

Abstract:

Association rules are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for association rules mining. In this paper, we propose an incremental association rules mining algorithm that integrates shocking interestingness criterion during the process of building the model. A new interesting measure called shocking measure is introduced. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. We implemented the proposed approach and experiment it with some public datasets and found the results quite promising.

Keywords: Knowledge discovery in databases (KDD), Data mining, Incremental Association rules, Domain knowledge, Interestingness, Shocking rules (SHR).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082779

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1515

References:


[1] Han, J. and Kamber, M.: Data Mining: Concepts and Techniques. San Francisco, Morgan Kauffmann Publishers, (2001).
[2] Dunham M. H.: Data Mining: Introductory and Advanced Topics. 1st Edition Pearson Education (Singapore) Pte. Ltd. (2003).
[3] Hand, D., Mannila, H. and Smyth, P.: Principles of Data Mining, Prentice-Hall of India Private Limited, India, (2001).
[4] Kaur H., Wasan. S. K, Al-Hegami A. S., Bhatnagar, V.: A Unified Approach for Discovery of Interesting Association Rules. To appear in Proceedings of Industrial Conference on Data Mining (ICDM), 2006.
[5] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Adaptive Constraint Pushing in Frequent Pattern Mining. In Proceedings of the 17th European Conference on PAKDD03. (2003).
[6] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: ExAMiner: Optimized Level-wise Frequent pattern Mining with Monotone Constraints. In Proceedings of the 3rd International Conference on Data Mining (ICDM03). (2003).
[7] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante: Anticipated Data Reduction in Constrained Pattern Mining. In Proceedings of the 7th PAKDD03. (2003).
[8] Klemetinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A. I.: Finding Interesting Rules from Large Sets of Discovered Association Rules. In Proceedings of the 3rd International Conference on Information and Knowledge Management. Gaithersburg, Maryland. (1994).
[9] Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the Subjective Interestingness of Association Rules. IEEE Intelligent Systems. (2000).
[10] Psaila, G.: Discovery of Association Rules Meta-Patterns. In Proceedings of 2nd International Conference on Data Warehousing and Knowledge Discovery (DAWAK99). (1999).
[11] Agrawal, R., Imielinski, T. and Swami, A.: Mining Association Rules between Sets of Items in Large Databases, In ACM SIGMOD Conference of Management of Data. Washington D.C., (1993).
[12] Hansel, G.: Sur le nombre des functions Boolenes Monotones den variables. C.R. Acad. Sci. Paris, 262(20):1088-1090 (in French). (1966).
[13] Ganti, V., Gehrke, J. and Ramakrishnan, R.: DEMON: Mining and Monitoring evolving data. In Proceeding of the 16th International Conference on Data Engineering, San Diego, USA. (2000).
[14] Lee, S., and Cheung, D.: Maintenance of discovered association rules. When to update? In Research Issues on Data Mining and Knowledge Discovery. (1997).
[15] Zaki, M. and Hsiao, C.: Charm: An efficient algorithm for closed itemset mining. In Proceeding of the 2nd SIAM International Conference on Data Mining, Arlington, USA. (2002).
[16] Cheung, D. W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered Association Rules in Large Databases: An Incremental Updating Technique, Proc. the International Conference On Data Engineering, (1996) 106-114.
[17] Cheung, D. W., Ng, V.T., Tam, B.W.: Maintenance of Discovered Knowledge: A case in Multi-level Association Rules, Proc. 2nd International Conference on Knowledge Discovery and Data Mining, (1996) 307-310.
[18] Cheung, D. W., Lee, S.D., Kao, B.: A general Incremental Technique for Mining Discovered Association Rules, Proc. International Conference on Database System for Advanced Applications, (1997) 185-194.
[19] Yafi, E., Alam, M.A., Biswas, R.: Development of Subjective Measures of Interestingness: From Unexpectedness to Shocking, Proceedings of World Academy of Science, Engineering and Technology Volume 26 December 2007 ISSN 1307-6884.