I need your valuable guidance about good literature and aiding tools.
Some Links May be helps to you
http://www.csee.umbc.edu/~hillol/DDMBIB/ddmbib_html
www.cs.technion.ac.il/~ranw/papers/wolff01sigmod.pdf
www.ijsrp.org/research-paper-1212/ijsrp-p1247.pdf
www.csc.lsu.edu/~huypham/papers/N127.pd
[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 | ]
[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 ]
Some paper Links
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4470359&abstractAccess=no&userType=inst
Some Links May be helps to you
http://www.csee.umbc.edu/~hillol/DDMBIB/ddmbib_html
www.cs.technion.ac.il/~ranw/papers/wolff01sigmod.pdf
www.ijsrp.org/research-paper-1212/ijsrp-p1247.pdf
www.csc.lsu.edu/~huypham/papers/N127.pd
[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 | ]
[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 ]
Some paper Links
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4470359&abstractAccess=no&userType=inst
Are the data structured or unstructured?
Is the collection to be analyzed retrospective in nature (already in the database), or will it be collected prospectively?
There are several approaches to structured data ( so the meaning of the data is preset) from one dataset, collected prospectively that I suggest to look at. However, this is not very recent, and not using distributed approaches. However, if you are able to combine the data from distributed storage into one pool for the analysis, it still would do I think. First analysis linkages between medical diagnoses and nursing diagnoses and care, and second identifies differences in such data / groups. It is in particular for the analytical approaches that i suggest this.
Griens AMGF, Goossen WTF, Kloot, WA van der (2001). Exploring the Nursing Minimum Data Set for the Netherlands Using Multidimensional Scaling Techniques. Journal of Advanced Nursing, 36(1):89-101.
van Beek L, Goossen WT, van der Kloot WA, (2005). Linking nursing care to medical diagnoses: Heterogeneity of patient groups. Int J Med Inform, 74, 926-936.
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