Abstract
Association rules mining from transaction-oriented databases is an important issue in data mining. Frequent pattern is crucial for association rules generation, time series analysis, classification, etc. There are two categories of algorithms that had been proposed, candidate set generate-and-test approach (Apriori-like) and Pattern growth approach. Many methods had been proposed to solve the association rules mining problem based on FP-tree instead of Apriori-like, since apriori-like algorithm scans the database many times. However, the computation time is costly when the database size is large with FP-tree data structure. Parallel and distributed computing is a good strategy to solve this circumstance. Some parallel algorithms had been proposed, however, most of them did not consider the load balancing issue. In this paper, we proposed a parallel and distributed mining algorithm based on FP-tree structure, Load Balancing FP-Tree (LFP-tree). The algorithm divides the item set for mining by evaluating the tree’s width and depth. Moreover, a simple and trusty calculate formulation for loading degree is proposed. The experimental results show that LFP-tree can reduce the computation time and has less idle time compared with Parallel FP-Tree (PFP-tree). In addition, it has better speed-up ratio than PFP-tree when number of processors grow. The communication time can be reduced by preserving the heavy loading items in their local computing node.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for Mining Association Rules in Large Database. In: Proceedings of the 20th International conference on Very Large Data Base, pp. 487–499 (1994)
Almaden, I.: Quest synthetic data generation code, http://www.almaden.ibm.com/cs/quest/syndata.html
Coenen, F., Leng, P., Ahmed, S.: Data structure for association rule mining: T-trees and P-trees. IEEE Transactions on Knowledge and Data Engineering 16(6), 774–778 (2004)
Gorodetsky, V., Karasaeyv, O., Samoilov, V.: Multi-agent Technology for Distributed Data Mining and Classification. In: Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology, pp. 438–441 (2003)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. J. of Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Holt, J.D., Chung, S.M.: Parallel mining of association rules from text databases on a cluster of workstations. In: Proceedings of 18th International Symposium on Parallel and Distributed Processing, p. 86 (2004)
Iko, P., Kitsuregawa, M.: Shared Nothing Parallel Execution of FP-growth. DBSJ Letters 2(1), 43–46 (2003)
Javed, A., Khokhar, A.: Frequent Pattern Mining on Message Passing Multiprocessor Systems. Distributed and Parallel database 16(3), 321–334 (2004)
Li, T., Zhu, S., Ogihara, M.: A New Distributed Data Mining Model Based on Similarity. Symposium on Applied Computing, pp. 432–436 (2003)
Lin, C.-R., Lee, C.-H., Chen, M.-S., Yu, P.S.: Distributed Data Mining in a Chain Store Database of Short Transactions. In: Conference on Knowledge Discovery in Data, pp. 576–581 (2002)
Park, J.S., Chen, M.-S., Yu, P.S.: An Effective Hash-Based Algorithm for Mining Association Rules. ACM SIGMOD Record 24(2), 175–186 (1995)
Tang, P., Turkia, M.P.: Parallelizing Frequent Itemset Mining with FP-Trees. Computers and Their Applications, pp. 30–35 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, KM., Zhou, J., Hsiao, W.C. (2007). Load Balancing Approach Parallel Algorithm for Frequent Pattern Mining. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2007. Lecture Notes in Computer Science, vol 4671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73940-1_63
Download citation
DOI: https://doi.org/10.1007/978-3-540-73940-1_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73939-5
Online ISBN: 978-3-540-73940-1
eBook Packages: Computer ScienceComputer Science (R0)