Abstract
Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and tested on the Apriori algorithm. In this paper we discuss and experimentally evaluate three strategies for concurrent processing of frequent itemset queries using FP-growth as a basic frequent itemset mining algorithm. The first strategy is Mine Merge, which does not depend on a particular mining algorithm and can be applied to FP-growth without modifications. The second is an implementation of the general idea of Common Counting for FP-growth. The last is a completely new strategy, motivated by identified shortcomings of the previous two strategies in the context of FP-growth.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Support Threshold
- Inductive Logic Programming
- Concurrent Processing
- Original Query
- Frequent Itemset Mining
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. of the 1993 ACM SIGMOD Conf. on Management of Data, ACM Press, New York (1993)
Agrawal, R., Mehta, M., Shafer, J., Srikant, R., Arning, A., Bollinger, T.: The Quest Data Mining System. In: Proc. of the 2nd Int’l Conference on Knowledge Discovery in Databases and Data Mining (1996)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int’l Conf. on Very Large Data Bases (1994)
Alsabbagh, J.R., Raghavan, V.V.: Analysis of common subexpression exploitation models in multiple-query processing. In: Proc. of the 10th ICDE Conference (1994)
Baralis, E., Psaila, G.: Incremental Refinement of Mining Queries. In: Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 173–182. Springer, Heidelberg (1999)
Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Ramon, J., Vandecasteele, H.: Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs. Journal of Artificial Intelligence Research 16 (2002)
Cheung, D.W., Han, J., Ng, V., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: Proc. of the 12th ICDE (1996)
Han, J., Pei, J.: Mining Frequent Patterns by Pattern-Growth: Methodology and Implications. SIGKDD Explorations (December 2000)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of the 2000 ACM SIGMOD Conf. on Management of Data, ACM Press, New York (2000)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-pattern Tree Approach. Data Mining and Knowledge Discovery: An International Journal 8(1) (2004)
Imielinski, T., Mannila, H.: A Database Perspective on Knowledge Discovery. Communications of the ACM 39(11) (1996)
Jarke, M.: Common subexpression isolation in multiple query optimization. In: Kim, W., Reiner, D.S. (eds.) Query Processing in Database Systems, Springer, Heidelberg (1985)
Jeudy, B., Boulicaut, J-F.: Using condensed representations for interactive association rule mining. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 225–236. Springer, Heidelberg (2002)
Jin, R., Sinha, K., Agrawal, G.: Simultaneous Optimization of Complex Mining Tasks with a Knowledgeable Cache. In: Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York (2005)
Meo, R.: Optimization of a Language for Data Mining. In: Proc. of the ACM Symposium on Applied Computing - Data Mining Track, ACM Press, New York (2003)
Morzy, T., Wojciechowski, M., Zakrzewicz, M.: Materialized Data Mining Views. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, Springer, Heidelberg (2000)
Nag, B., Deshpande, P.M., DeWitt, D.J.: Using a Knowledge Cache for Interactive Discovery of Association Rules. In: Proc. of the 5th KDD Conference (1999)
Pei, J., Han, J.: Can We Push More Constraints into Frequent Pattern Mining? In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York (2000)
Roy, P., Seshadri, S., Sundarshan, S., Bhobe, S.: Efficient and Extensible Algorithms for Multi Query Optimization. ACM SIGMOD Intl. Conference on Management of Data (2000)
Savasere, A., Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proc. 21th Int’l Conf. Very Large Data Bases (1995)
Sellis, T.: Multiple-query optimization. ACM Transactions on Database Systems 13(1) (1988)
Wojciechowski, M., Zakrzewicz, M.: Evaluation of Common Counting Method for Concurrent Data Mining Queries. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, Springer, Heidelberg (2003)
Wojciechowski, M., Zakrzewicz, M.: Data Mining Query Scheduling for Apriori Common Counting. In: Proc. of the Sixth International Baltic Conference on Databases and Information Systems (2004)
Wojciechowski, M., Zakrzewicz, M.: Evaluation of the Mine Merge Method for Data Mining Query Processing. In: Benczúr, A.A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, Springer, Heidelberg (2004)
Wojciechowski, M., Zakrzewicz, M.: On Multiple Query Optimization in Data Mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wojciechowski, M., Galecki, K., Gawronek, K. (2007). Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth. In: Džeroski, S., Struyf, J. (eds) Knowledge Discovery in Inductive Databases. KDID 2006. Lecture Notes in Computer Science, vol 4747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75549-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-75549-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75548-7
Online ISBN: 978-3-540-75549-4
eBook Packages: Computer ScienceComputer Science (R0)