Abstract
The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All of these algorithms are highly tuned to take advantage of the unique properties of their associated constraints, and so they are not very compatible with other constraints. We present a more unified view of mining constrained itemsets such that most existing algorithms can be easily extended to handle constraints for which they were not designed a-priori. We apply this technique to mining itemsets with restrictions on their variance — a problem that has been open for several years in the data mining community.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proc. SIGMOD 1993, pp. 207–216. ACM Press, New York (1993)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, ch. 12, pp. 307–328. AAAI/MIT Press, Cambridge (1996)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. VLDB 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Examiner: Optimized level-wise frequent pattern mining with monotone constraint. In: ICDM, pp. 11–18. IEEE Computer Society, Los Alamitos (2003)
Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante: Anticipated data reduction in constrained pattern mining. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 59–70. Springer, Heidelberg (2003)
Boulicaut, J., Jeudy, B.: Using constraints during set mining: Should we prune or not (2000)
Boulicaut, J.-F., Jeudy, B.: Mining free itemsets under constraints. In: International Database Engineering and Application Symposium, pp. 322–329 (2001)
Bucila, C., Gehrke, J.E., Kifer, D., White, W.: Dualminer: A dual-pruning algorithm for itemsets with constraints. In: Proc. SIGKDD 2002, Edmonton, Alberta, Canada (July 2002)
Delis, A., Faloutsos, C., Ghandeharizadeh, S. (eds.): SIGMOD 1999, Philadephia, Pennsylvania, USA. ACM Press, New York (1999)
Gunopulos, D., Mannila, H., Khardon, R., Toivonen, H.: Data mining, hypergraph transversals, and machine learning. In: Proc. PODS 1997, pp. 209–216 (1997)
Hipp, J., Guntzer, U.: Is pushing constraints deeply into the mining algorithms really what we want? SIGKDD Explorations 4(1), 50–55 (2002)
Lakshmanan, L.V.S., Ng, R.T., Han, J., Pang, A.: Optimization of constrained frequent set queries with 2-variable constraints. In: Delis, et al. (eds.) [9], pp. 157–168
Leung, C.K.-S., Lakshmanan, L.V., Ng, R.T.: Exploiting succinct constraints using fp-trees. SIGKDD Explorations 4(1), 31–39 (2002)
Ng, R.T., Lakshmanan, L.V.S., Han, J., Mah, T.: Exploratory mining via constrained frequent set queries. In: Delis, et al. (eds.) [9], pp. 556–558
Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained association rules. In: Haas, L.M., Tiwary, A. (eds.) Proc. SIGMOD 1998, pp. 13–24. ACM Press, New York (1998)
Pei, J., Han, J.: Can we push more constraints into frequent pattern mining? In: ACM SIGKDD Conference, pp. 350–354 (2000)
Pei, J., Han, J.: Constrained frequent pattern mining: A pattern-growth view. SIGKDD Explorations 4(1), 31–39 (2002)
Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent item sets with convertible constraints. In: ICDE 2001, pp. 433–442. IEEE Computer Society, Los Alamitos (2001)
Perng, C.-S., Wang, H., Ma, S., Hellerstein, J.L.: Discovery in multi-attribute data with user-defined constraints. SIGKDD Explorations 4(1), 56–64 (2002)
Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: Proc. KDD 1997 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kifer, D., Gehrke, J., Bucila, C., White, W. (2006). How to Quickly Find a Witness. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_11
Download citation
DOI: https://doi.org/10.1007/11615576_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31331-1
Online ISBN: 978-3-540-31351-9
eBook Packages: Computer ScienceComputer Science (R0)