Abstract
The notion of preference naturally occurs in every context where one talks about human decisions or choice. Users, faced with a huge amount of data but often not equipped with a complete knowledge of the nature of the data, seek ways to obtain not necessarily all but the best or most preferred solutions. In this paper, we study preferences in the context of frequent pattern mining using the utility function approach. We also seek to provide a framework for investigating data mining problems involving preferences. We consider the problem of preference frequent pattern mining and N-best frequent pattern mining. We define preferences analytically, investigate their properties and classify them. We also provide some preference frequent pattern mining algorithms and show how they can be used for efficient N-best data mining.
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References
Hansson, S.O.: Prefernce Logic. In: Gabbay, D. (ed.) Handbook of Philosophical Logic, vol. 8 (2001)
Mantha, S.M.: First-Order Preference Theories and their Applications. Ph.D. thesis, University of Utah (1991)
von Wright, G.H.: The Logic of Preference. Edinburgh University Press (1963)
Boutilier, C., Braman, R.I., Hoos, H.H., Poole, D.: Reasoning with Conditional Ceteris Paribus Preference Statements. In: Symposium on Uncertainty in Artificial Intelligence (1999)
Tan, S.-W., Pearl, J.: Specification and Evaluation of Preferences under Uncertainty. In: International Conference on Principles of Knowledge Representation and Reasoning (1994)
Wellman, M.P., Doyle, J.: Preferential Semantics for Goals. In: National Conference on Artificial Intelligence (1991)
Fishburn, P.: Preference Structures and their Numerical Representations. Theoretical Computer Science 217, 359–383 (1999)
Fishburn, P.: Utility Theory for Decision Making. Wiley & Sons, Chichester (1970)
Agrawal, R., Wimmers, E.L.: A Framework for Expressing and Combining Preferences. In: ACM SIGMOD International Conference on Management of Data, pp. 297–306 (2000)
Borzsonyi, S., Kossman, D., Stocker, K.: The Skyline Operator. In: IEEE International Conference on Data Engineering, pp. 421–430 (2001)
Govindarajan, K., Jayaraman, B., Mantha, S.: Preference Queries in Deductive Databases. New Generation Computing, 57-86 (2001)
Lacroix, M., Lavency, P.: Preferences: Putting More Knowledge into Queries. In: International Conference on Very Large Databases, pp. 217–225 (1987)
Chomicki, J.: Querying with Intrinsic Preferences. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, p. 34. Springer, Heidelberg (2002)
Jagannath, S.: Utility Guided Pattern Mining, NCSU MS Thesis (2003)
Pei, J.: Private communication
Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent itemsets with convertible constraints. In: Proc. 2001 Int. Conf. Data Engineering (ICDE 2001) (2001)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 2000) (2000)
Han, J., Pei, J.: Can we push more constraints into frequent pattern mining. In: Proc. ACM-SIGKDD Int. Conf. Knowlegde Discovery in Databases (KDD 2000) (2000)
Han, J., Zhou, S.: Profit Mining: From Patterns to Auctions. In: EDRT 2002 (2002)
Cheung, Y.-L., Fu, A.W.-C.: Mining Frequent Itemsets without Support Threshold: with and without Item Constraints
Sawai, R., Tsukamoto, M., Loh, Y.-H., Terada, T., Nishio, S.: Functional Properties of Information Filtering. In: Proc. of the 27th VLDB Conference (2001)
Wang, W., Yang, J., Yu, P.: WAR: Weighted Association Rules for Item Intensities. Knowledge and Information Systems Journal (KAIS) 6, 203–229 (2004)
Yao, Y., Chen, Y., Yang, X.: A Measurement-Theoretic Foundation of Rule Interestingness Evaluation. In: ICDM 2003 Workshop on Foundations of Data Mining (2003)
Fretas, A.A.: On Rule Interestingness measures. Knowledge-Based Systems 12, 309–315 (1999)
Lin, T.Y., Yao, Y.Y., Louie, E.: Value Added Association Rules. In: Proc. of PAKDD (2002)
Silberschatz, A., Tuzhilin, A.: On Subjective Measures of Interestingness in Knowl- edge Discovery. In: Proc. of KDD (1995)
Yao, Y., Zhong, N.: An Analysis of Quantitative Measures associated with Rules. In: Proc. of PAKDD (1999)
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Braynova, E., Pendharkar, H. (2005). Frequent Pattern Mining with Preferences–Utility Functions Approach. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_38
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DOI: https://doi.org/10.1007/11425274_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25878-0
Online ISBN: 978-3-540-31949-8
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