Abstract
The task of detecting those association rules which are interesting within the vast set of discovered ones still is a major research hallenge in data mining. Although several possible solutions have been proposed, they usually require a user to be aware what he knows, to have a rough idea what he is looking for, and to be able to specify this knowledge in advance. In this paper we compare the task of finding the most relevant rules with the task of finding the most relevant documents known from Information Retrieval. We propose a novel and flexible method of relevance feedback for association rules which leverages technologies from Information Retrieval, like document vectors, term frequencies and similarity calculations. By acquiring a user’s preferences our approach builds a repository of what he considers to be (non-)relevant. By calculating and aggregating the similarities of each unexamined rule with the rules in the repository we obtain a relevance score which better reflects the user’s notion of relevance with each feedback provided.
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References
Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swami. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD 1993, pages 207–216, Washington, DC, 1993.
Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pages 487–499. Morgan Kaufmann, 12–15 1994.
KeWang, Yuelong Jiang, and Laks V. S. Lakshmanan. Mining unexpected rules by pushing user dynamics. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 246–255, 2003.
Dong Xin, Xuehua Shen, Qiaozhu Mei, and Jiawei Han. Discovering interesting patterns through user’s interactive feedback. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 773–778, New York, NY, USA, 2006. ACM Press.
Ricardo A. Baeza-Yates and Berthier A. Ribeiro-Neto. Modern Information Retrieval. ACM Press / Addison-Wesley, 1999.
Pang-Ning Tan, Vipin Kumar, and Jaideep Srivastava. Selecting the right objective measure for association analysis. Information Systems, 29(4):293–313, 2004.
Abraham Silberschatz and Alexander Tuzhilin. What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering, 8(6):970–974, 1996.
G. Piatesky-Shapiro and C. J. Matheus. The interestingness of deviations. In Proceedings AAAI workshop on Knowledge Discovery in Databases, pages 25–36, 1994.
Balaji Padmanabhan and Alexander Tuzhilin. Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27, 1999.
Balaji Padmanabhan and Alexander Tuzhilin. Small is beautiful: discovering the minimal set of unexpected patterns. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 54–63, 2000.
Balaji Padmanabhan and Alexander Tuzhilin. Knowledge refinement based on the discovery of unexpected patterns in data mining. Decision Support Systems, 33(3):309–321, 2002.
Bing Liu, Wynne Hsu, and Shu Chen. Using general impressions to analyze discovered classi?cation rules. In Proceedings of the 3rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 31–36, 1997.
Bing Liu, Wynne Hsu, Shu Chen, and Yiming Ma. Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15(5):47–55, 2000.
David B. Fogel. The advantages of evolutionary computation. In D. Lundh, B. Olsson, and A. Narayanan, editors, Bio-Computing and Emergent Computation. World Scientific Press, Singapore, 1997.
Gerard Salton. The SMART Information Retrieval System. Prentice Hall,Englewood Cliffs, NJ, 1971.
Tommi Jaakkola and Hava Siegelmann. Active information retrieval. In Advances in Neural Information Processing Systems 14, pages 777–784. MIT Press, 2001.
Mirko Boettcher, Detlef Nauck, Dymitr Ruta, and Martin Spott. Towards a framework for change detection in datasets. In Proceedings of the 26th SGAI International Conference on Innovative Techniques and Applications of Arti?cial Intelligence, pages 115–128. Springer, 2006.
Gerard Salton and Chris Buckley. Term weighting approaches in automatic text retrieval. Information Processing and Management, 5(24):513– 523, 1987.
Ronald R. Yager. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern., 18(1):183– 190, 1988.
Ronald R. Yager. On the inclusion of importances in owa aggregations. In The ordered weighted averaging operators: theory and applications, pages 41–59, Norwell, MA, USA, 1997. Kluwer Academic Publishers.
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Ruß, G., Nauck, D., Böttcher, M., Kruse, R. (2008). Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval. In: Bramer, M., Coenen, F., Petridis, M. (eds) Research and Development in Intelligent Systems XXIV. SGAI 2007. Springer, London. https://doi.org/10.1007/978-1-84800-094-0_19
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DOI: https://doi.org/10.1007/978-1-84800-094-0_19
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