Abstract
Discovering interesting associations of events is an important data mining task. In many real applications, the notion of association, which defines how events are associated, often depends on the particular application and user requirements. This motivates the need for a general framework that allows the user to specify the notion of association of his/her own choices. In this paper we present such a framework, called the UDA mining (User-Defined Association Mining). The approach is to define a language for specifying a broad class of associations and yet efficient to be implemented. We show that (1) existing notions of association mining are instances of the UDA mining, and (2) many new ad-hoc association mining tasks can be defined in the UDA mining framework.
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References
R. Agrawal, T. Imielinski, and A. Swami: Mining Association Rules between Sets of Items in Large Datasets. SIGMOD 1993, 207–216
R. Agrawal and R. Srikant: Fast Algorithm for Mining Association Rules. VLDB 1994, 487–499
C.C. Aggarwal and P.S. Yu: A New Framework for Itemset Generation. PODS 1998, 18–24
S. Brin, R. Motwani, and C. Silverstein: Beyond Market Baskets: Generalizing Association Rules to Correlations. SIGMOD 1997, 265–276
G.F. Cooper: A Simple Constraint-based Algorithm for Efficiently Mining Observational Databases for Causal Relationships. Data Mining and Knowledge Discovery, No. 1, 203–224, 1997, Kluwer Academic Publishers
G. Dong, J. Li: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. SIGKDD 1999, 43–52
D. Heckerman: Bayesian Networks for Data Mining. Data Mining and Knowledge Discovery, Vol. 1, 1997, 79–119
J. Han, Y. Fu, W. Wang, K. Koperski, O. Zaiane: DMQL: A Data Mining Query Language for Relational Databases. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1996, 27–34
T. Imielinski and H. Mannila: A Database Perspective on Knowledge Discovery. Communications of ACM, 39(11), 58–64, 1996
T. Imielinski, A. Virmani, and A. Abdulghani: DataMine: Application Programming Interface and Query Language for Database Mining. KDD 1996, 256–261
H. Mannila: Methods and Problems in Data Mining. International Conference on Database Theory, 1997, 41–55, Springer-Verlag
R. Meo, G. Psaila, S. Ceri: A New SQL-like Operator for Mining Association Rules. VLDB 1996, 122–133
R.T. Ng, L.V.S. Lakshmanan, J. Han, A Pang: Exploratory Mining and Pruning Optimizations of Constrained Associations Rules. SIGMOD 1998, 13–24
C. Silverstein, S. Brin, R. Motwani, J. Ullman: Scalable Techniques for Mining Causal Structures. VLDB 1998, 594–605
P. Smyth, R.M. Goodman: An Information Theoretic Approach to Rule Induction from Databases. IEEE Transactions on Knowledge and Data Engineering, Vol. 4,No. 4, 301–316, August 1992.
R. Srikant, Q. Vu, and R. Agrawal: Mining Association Rules with Item Constraints. KDD 1997, 67–73
D. Tsur, J. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, A. Rosenthal: Query Flocks: a Generalization of Association-Rule Mining. SIGMOD 1998, 1–12
T.D. Wickens: Multiway Contingency Tables Analysis for the Social Sciences. Lawrence Brlbaum Associates, 1989.
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© 2001 Springer-Verlag Berlin Heidelberg
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Wang, K., He, Y. (2001). User-Defined Association Mining. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_41
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DOI: https://doi.org/10.1007/3-540-45357-1_41
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