Distributed Association Rule Mining Bibliography

[1] R. C. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for Generation of Frequent Item Sets. Journal of Parallel and Distributed Computing, 61(3):350-371, 2001.
[ bib | http://citeseer.nj.nec.com/agarwal99tree.html ]
[2] R. Agrawal and J. C. Shafer. Parallel Mining of Association Rules. IEEE Transactions On Knowledge And Data Engineering, 8:962-969, 1996.
[ bib | http://citeseer.nj.nec.com/agrawal96parallel.html ]
[3] V. S. Ananthanarayana, D. K. Subramanian, and M. N. Murty. Scalable, Distributed and Dynamic Mining of Association Rules. In Proceedings of HIPC'00, pages 559-566, Bangalore, India, 2000.
[ bib | ]
[4] A. Atramentov, H. Leiva, and V. Honavar. A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments. In Proceedings of the Thirteenth International Conference on Inductive Logic Programming, Berlin, 2003. Springer-Verlag.
[ bib | http://www.cs.iastate.edu/~honavar/Papers/ilpfinal.pdf ]
[5] H. Cheng, P. Tan, S. Jon, and W. Punch. Recommendation via Query Centered Random Walk on K-partite Graph. In Proceedings of the IEEE International Conference on Data Mining (ICDM '07), pages 457-462, Omaha, NE, 2007.
[ bib | ftp://ftp.computer.org/press/outgoing/proceedings/icdm07/Data/3018a457.pdf ]
[6] D. Cheung and Y. Xiao. Effect of Data Skewness in Parallel Mining of Association Rules. In 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 48-60, Melbourne, Australia, April 1998.
[ bib | ]
[7] D. W. Cheung, J. Han, V. T. Ng, A. W. Fu, and Y. Fu. A Fast Distributed Algorithm for Mining Association Rules. In Proceedings of 1996 International Conference on Parallel and Distributed Information Systems (PDIS'96), pages 31-44, Miami, FL, 1996.
[ bib | http://portal.acm.org/citation.cfm?id=383194 ]
[8] D. W. Cheung, V. T. Ng, A. W. Fu, and Y. Fu. Efficient Mining of Association Rules in Distributed Databases. IEEE Transactions On Knowledge And Data Engineering, 8:911-922, 1996.
[ bib | http://citeseer.ist.psu.edu/cheung96efficient.html ]
[9] F. Coenen, P. Leng, and A. Shakil. T-trees, Vertical Partitioning and Distributed Association Rule Mining. In The Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, FL, November 2003.
[ bib | http://csdl.computer.org/comp/proceedings/icdm/2003/1978/00/19780513abs.htm ]
[10] A. Javed and A. Khokhar. Frequent Pattern Mining on Message Passing Multiprocessor Systems. Distributed and Parallel Databases , 16(3):321-334, November 2004.
[ bib | http://dx.doi.org/10.1023/B:DAPD.0000031634.19130.bd ]
[11] Asif Javed and Ashfaq Khokhar. Frequent pattern mining on message passing multiprocessor systems. Distributed and Parallel Databases, 16(3):321 - 334, November 2004.
[ bib | ]
[12] V. C. Jensen and N. Soparkar. Frequent Itemset Counting Across Multiple Tables. In 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 49-61, 2000.
[ bib | http://www.eecs.umich.edu/~soparkar/Ddmprog/pakdd2000/paper.pdf ]
[13] S. Li, T. Wu, and W. M. Pottenger. Distributed Higher Order Association Rule Mining Using Information Extracted from Textual Data. SIGKDD Exploration, 7(1):26-35, 2005.
[ bib | http://www.acm.org/sigs/sigkdd/explorations/issues/7-1-2005-06/5-Li.pdf ]
[14] A. Manjhi, V. Shkapenyuk, K. Dhamdhere, and C. Olston. Finding (Recently) Frequent Items in Distributed Data Streams. In Proceedings of the 21st International Conference on Data Engineering (ICDE'05), Tokyo, Japan, April 2005.
[ bib | http://reports-archive.adm.cs.cmu.edu/anon/2004/CMU-CS-04-121.pdf ]
[15] A. M. Manning and J. A. Keane. Data Allocation Algorithm for Parallel Association Rule Discovery. In The Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2001), Hong Kong, China, April 2001.
[ bib | http://www.cs.man.ac.uk/cnc/staff/anna/p0065.ps ]
[16] S. Nestorov. Mining Qualified Association Rules in Distributed Databases. In Workshop on Data Mining and Exploration Middleware for Distributed and Grid Computing, Minneapolis, MN, September 2003.
[ bib | ]
[17] J. S. Park, M.-S.Chen, and P. S. Yu. Efficient Parallel Data Mining for Association Rules. In Proceedings of ACM International Conference on Information and Knowledge Management, pages 31-36, Baltimore, MD, November 1995.
[ bib | http://portal.acm.org/citation.cfm?id=221270.221320 ]
[18] S. Parthasarathy, M. Zaki, and W. Li. Memory Placement Techniques for Parallel Association Mining. In The Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, August 1998.
[ bib | ]
[19] S. Parthasarathy, M. J. Zaki, M. Ogihara, and W. Li. Parallel Data Mining for Association Rules on Shared-Memory Systems. Knowledge and Information Systems, 3(1):1-29, February 2001.
[ bib | http://portal.acm.org/citation.cfm?id=545301 ]
[20] I. Pramudiono and M. Kitsuregawa. Parallel FP-Growth on PC Cluster. In Proceedings of the Seventh Pacific-Asia Conference of Knowledge Discovery and Data Mining (PAKDD03), pages 467-473, Seoul, Korea, April - May 2003.
[ bib | http://www.tkl.iis.u-tokyo.ac.jp/~iko/ ]
[21] Iko Pramudiono and Masaru Kitsuregawa. Parallel FP-Growth on PC cluster. In Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference (PAKDD), Seoul, Korea, April-May 2003.
[ bib | http://www.springerlink.com/content/y5tmwld5rjqun826/ ]
[22] A. Schuster and R. Wolff. Communication-Efficient Distributed Mining of Association Rules. Data Mining and Knowledge Discovery, 8(2), March 2004.
[ bib | ]
[23] Assaf Schuster and Ran Wolff. Communication Efficient Distributed Mining of Association Rules. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, volume 30, pages 473-484, California, USA, June 2001.
[ bib | http://www.cs.umbc.edu/~ranw/papers/wolff01sigmod.pdf ]
[24] Assaf Schuster, Ran Wolff, and Bobi Gilburd. Privacy-Preserving Association Rule Mining in Large-Scale Distributed Systems. In Proceedings of Cluster Computing and the Grid (CCGrid), 2004.
[ bib | http://www.cs.technion.ac.il/~assaf/publications/P4ARM_CCGrid.pdf ]
[25] Assaf Schuster, Ran Wolff, and Dan Trock. A High-Performance Distributed Algorithm for Mining Association Rules . In Third IEEE International Conference on Data Mining, Florida , USA, November 2003.
[ bib | http://www.cs.technion.ac.il/~ranw/papers/wolff03icdm1.pdf ]
[26] D. B. Skillicorn. Parallel Frequent Set Counting. Parallel Computing, 28(5):815-825, May 2002.
[ bib | http://portal.acm.org/citation.cfm?id=605729 ]
[27] D. B. Skillicorn. Parallel frequent set counting. Distributed and Parallel Databases, 28(5):815 - 825, May 2002.
[ bib | ]
[28] S. Stolfo, H. Dewan, D. Ohsie, and M. Hernandez. A Parallel and Distributed Environment for Database Rule Processing, Open Problems and Future Directions. In Emerging Trends in Database and Knowledge-based Machines IEEE Press, 1995.
[ bib | http://www.computer.org/cspress/CATALOG/bp06552/toc.htm ]
[29] R. Wolff, A. Schuster, and D. Trock. A High-Performance Distributed Algorithm for Mining Association Rules. In The Third IEEE International Conference on Data Mining (ICDM'03), November 2003.
[ bib | http://csdl.computer.org/comp/proceedings/icdm/2003/1978/00/19780291abs.htm ]
[30] Ran Wolff and Assaf Schuster. Association Rule Mining in Peer-to-Peer Systems . In Third IEEE International Conference on Data Mining, Melbourne, FL, November 2003.
[ bib | http://www.cs.technion.ac.il/~ranw/papers/wolff03icdm2.pdf ]
[31] O. Zaiane, M. El-Hajj, and P. Lu. Fast Parallel Association Rules Mining without Candidacy Generation. In IEEE 2001 International Conference on Data Mining (ICDM'2001), pages 665-668, 2001.
[ bib | http://www.cs.ualberta.ca/~zaiane/postscript/icdm01.pdf ]
[32] M. Zaki. Parallel and Distributed Association Mining: A Survey. IEEE Concurrency, 1999.
[ bib | http://www.cs.rpi.edu/~zaki/PS/concurrency.pdf ]
[33] M. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors. In Proceedings of Supercomputing'96, pages 17-22, Pittsburg, PA, November 1996.
[ bib | http://citeseer.ist.psu.edu/zaki96parallel.html ]