Abstract
Association Rule Mining (ARM) is an active data mining research area. However, most ARM algorithms cater to a centralized environment where no external communication is required. Distributed Association Rule Mining (DARM) algorithms aim to generate rules from different datasets spread over various geographical sites; hence, they require external communications throughout the entire processor. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. DARM algorithms must reduce communication costs. In this paper, a new solution is proposed to reduce the size of message exchanges. Our solution also reduces the size of average transactions and datasets that leads to reduction of scan time, which is very effective in increasing the performance of the proposed algorithm. Our performance study shows that this solution has a better performance over the direct application of a typical sequential algorithm.
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., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Database. In: Proc. 20th Int’l Conf. Very Large Databases (VLDB 1994), p. 40. Morgan Kaufmann, San Francisco (1994)
Agrawal, R., Shafer, J.C.: Parallel Mining of Association Rules. IEEE Tran. Knowledge and Data Eng. 8(6), 962–969 (1996), http://csdl.computer.org
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Inkeri Verkamo, A.: Fast Discovery of Association Rules in Large Databases. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)
Savasere, A., Omiecinski, E., Navathe, S.B.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proc. 21st Int’l Conf. Very Large Databases (VLDB 1994), pp. 432–444. Morgan Kaufmann, San Francisco (1995)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. ACM SIGMOD Int’l. Conf. Management of data, pp. 1–12. ACM Press, New York (2000)
Zaki, M.J., Pin, Y.: Introduction: Recent Developments in Parallel and Distributed Data Mining. J. Distributed and Parallel Databases 11(2), 123–127 (2002)
Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE Trans. Knowledge and Data Eng. 12(2), 372–390 (2000), http://csdl.computer.org
Zaki, M.J., et al.: Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors, Tech. report TR 618, Computer Science Dept., Univ. of Rochester (1996)
Cheung, D.W., Ng, V.T., Fu, A.W., Fu, Y.: Efficient Mining of Association Rules in Distributed Databases. IEEE transactions on Knowledge and Data Engineering (1996)
Cheung, D.W., et al.: A Fast Distributed Algorithm for Mining Association Rules. In: Proc. Parallel and Distributed Information Systems, pp. 31–42. IEEE CS Press, Los Alamitos (1996), http://csdl.computer.org/comp/proceedings/pdis/1996/7475/00/74750031abs.htm
Jarai, Z., Virmani, A., Iftode, L.: Towards a cost-effective parallel data mining approach. In: Workshop on High Performance Data Mining (held in conjunction with IPPS 1998) (1998)
Schuster, A., Wolff, R.: Communication-efficient distributed mining of association rules. In: Proc. Of the 2001 ACM SIGMOD Int’l. Conference on Management of Data, Santa Barbara, California, May 2001, pp. 473–484 (2001)
Zaiane, R.O.R., El-Hajj, M., Lu, P.: Fast parallel association rules mining without candidacy generation. In: IEEE 2001 International Conference on Data Mining (ICDM 2001) (2001)
Ganti, V., Gehrke, J., Ramakrishnan, R.: Mining Very Large Databases. IEEE, Los Alamitos (1999)
Han, E.-H.S., Karypis, G., Kumar, V.: Scalable parallel data mining for association rules. IEEE Trans action on Knowledge and Data Engineering (2000)
Shintani, Kitsuregawa: Hash based parallel algorithms for mining association rules. In: PDIS: International Conference on Parallel and Distributed Information Systems. IEEE Computer Society Technical Committee on Data Engineering, and ACM SIGMOD (1996)
Orlando, S., Palmerini, P., Perego, R., Silvestri, F.: A scalable multi-strategy algorithm for counting frequent sets. In: 5th Int. Workshop on High Perf. Data Mining HPDM (2002)
Zaki, M.J.: Parallel and distributed association mining. In: IEEE Concurrency (1999)
Schuster, A., Wol, R., Trock, D.: A high-performance distributed algorithm for mining association rules. In: Proc. IEEE International Conference on Data Mining ICDM 2003 (2003)
Ashrafi, M.Z.: Monash University ODAM: An Optimized Distributed Association Rule Mining Algorithm. In: IEEE DISTRIBUTED SYSTEMS ONLINE 1541-4922 © 2004. IEEE Computer Society, Los Alamitos (2004) vol. 5(3); March 2004, F.Mills, Statistical Methods, Pitman, (1955)
Park, J.S., Chen, M.-S., Yu, P.S., Watson, T.J.: Efficient Parallel Data Mining for Association Rules Research Center Yorktown Heights, New York, p. 10598.
Park, J.S., Chen, M., Yu, P.S.: An Effective Hash Based Algorithm for Mining Association Rules. In: Proc. 1995 ACM SIGMOD Int’l Conf. Management of Data. ACM Press, New York (1995)
Han, J.W., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, Dept. of Information and Computer Science, University of California, Irvine (1998)
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
Farzanyar, Z., Kangavari, M., Hashemi, S. (2006). An Efficient Distributed Algorithm for Mining Association Rules. In: Guo, M., Yang, L.T., Di Martino, B., Zima, H.P., Dongarra, J., Tang, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2006. Lecture Notes in Computer Science, vol 4330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946441_38
Download citation
DOI: https://doi.org/10.1007/11946441_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68067-3
Online ISBN: 978-3-540-68070-3
eBook Packages: Computer ScienceComputer Science (R0)