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Mining articulate association rules from closed item sets: A Counter Support Measurement approach

by Anurag Choubey, Ravindra Patel, J.l. Rana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 19
Year of Publication: 2012
Authors: Anurag Choubey, Ravindra Patel, J.l. Rana
10.5120/7051-9715

Anurag Choubey, Ravindra Patel, J.l. Rana . Mining articulate association rules from closed item sets: A Counter Support Measurement approach. International Journal of Computer Applications. 46, 19 ( May 2012), 25-31. DOI=10.5120/7051-9715

@article{ 10.5120/7051-9715,
author = { Anurag Choubey, Ravindra Patel, J.l. Rana },
title = { Mining articulate association rules from closed item sets: A Counter Support Measurement approach },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 19 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number19/7051-9715/ },
doi = { 10.5120/7051-9715 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:11.345937+05:30
%A Anurag Choubey
%A Ravindra Patel
%A J.l. Rana
%T Mining articulate association rules from closed item sets: A Counter Support Measurement approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 19
%P 25-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the previous works it has been observed that a frequent item set mining algorithm are supposed to mine the closed ones as the finish results in a compact and a complete progress set and enhanced potency. However, the latest closed item set mining algorithms works with both candidate maintenance and check paradigm hand in hand, which proves to be friendlier in runtime, as in case of area usage when support threshold is a reduced entity or the item sets gets long. In this paper, we have shown, CEG&REP with CSM (Counter Support Measurement) that is supposed to be a more efficient approach which can be utilized for mining articulate association rules from closed sequences. This approach outfits a exclusive rule coherency checking format with CSM, further that is laid mostly on another approach termed as Sequence Graph protruding which is termed as "Concurrent Edge Prevision and Rear Edge Pruning", hereby referred as CEG&REP. Moreover, we have pronounced a novel CSM methodology to crop rules which in turn seems to formulate articulate rules. The performance of CEG&REP with CSM (Counter Support Measurement) is tested on a whole observation having scrubby and dense real-life information, the tests have shown that approach of CEG&REP performs in a more efficient manner as compared to the previous versions as the CEG&REP approach takes less memory space and is swifter than the algorithms which were used in past works.

References
  1. F. Masseglia, F. Cathala, and P. Poncelet, The psp approach for mining sequential patterns. In PKDD'98, Nantes, France, Sept. 1995.
  2. R. Srikant, and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements. In EDBT'96, Avignon, France, Mar. 1996.
  3. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, FreeSpan: Frequent pattern-projected sequential pattern mining . In SIGKDD'00, Boston, MA, Aug. 2000.
  4. M. Zaki, SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning, 42:31-60, Kluwer Academic Pulishers, 2001.
  5. J. Pei, J. Han, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In ICDE'01, Heidelberg, Germany, April 2001.
  6. J. Ayres, J. Gehrke, T. Yiu, and J. Flannick, Sequential PAttern Mining using a Bitmap Representation. In SIGKDD'02, Edmonton, Canada, July 2002.
  7. M. Garofalakis, R. Rastogi, and K. Shim, SPIRIT: Sequential PAttern Mining with regular expression constraints. In VLDB'99, San Francisco, CA, Sept. 1999.
  8. J. Pei, J. Han, and W. Wang, Constraint-based sequential pattern mining in large databases. In CIKM'02, McLean, VA, Nov. 2002.
  9. M. Seno, G. Karypis, SLPMiner: An algorithm for finding frequent sequential patterns using lengthdecreasing support constraint. In ICDM'02,, Maebashi, Japan, Dec. 2002.
  10. H. Mannila, H. Toivonen, and A. I. Verkamo, Discovering frequent episodes in sequences . In SIGKDD'95, Montreal, Canada, Aug. 1995.
  11. B. Ozden, S. Ramaswamy, and A. Silberschatz, Cyclic association rules. In ICDE'98, Olando, FL, Feb. 1998.
  12. C. Bettini, X. Wang, and S. Jajodia, Mining temporal relationals with multiple granularities in time sequences. Data Engineering Bulletin, 21(1):32-38, 1998.
  13. J. Han, G. Dong, and Y. Yin, Efficient mining of partial periodic patterns in time series database. In ICDE'99, Sydney, Australia, Mar. 1999.
  14. J. Yang, P. S. Yu, W. Wang and J. Han, Mining long sequential patterns in a noisy environment. In SIGMOD' 02, Madison, WI, June 2002.
  15. N. Pasquier, Y. Bastide, R. Taouil and L. Lakhal, Discoving frequent closed itemsets for association rules. In ICDT'99, Jerusalem, Israel, Jan. 1999.
  16. M. Zaki, and C. Hsiao, CHARM: An efficient algorithm for closed itemset mining. In SDM'02, Arlington, VA, April 2002.
  17. X. Yan, J. Han, and R. Afshar, CloSpan: Mining Closed Sequential Patterns in Large Databases. In SDM'03, San Francisco, CA, May 2003.
  18. J. Wang, J. Han, and J. Pei, CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets. In KDD'03, Washington, DC, Aug. 2003.
  19. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB'94, Santiago, Chile, Sept. 1994.
  20. J. Pei, J. Han, and R. Mao, CLOSET: An efficient algorithm for mining frequent closed itemsets . In DMKD'01 workshop, Dallas, TX, May 2001.
  21. J. Han, J. Wang, Y. Lu, and P. Tzvetkov, Mining Top- K Frequent Closed Patterns without Minimum Support. In ICDM'02, Maebashi, Japan, Dec. 2002.
  22. P. Aloy, E. Querol, F. X. Aviles and M. J. E. Sternberg, Automated Structure-based Prediction of Functional Sites in Proteins: Applications to Assessing the Validity of Inheriting Protein Function From Homology in Genome Annotation and to Protein Docking. Journal of Molecular Biology, 311, 2002.
  23. R. Agrawal, and R. Srikant, Mining sequential patterns. In ICDE'95, Taipei, Taiwan, Mar. 1995.
  24. I. Jonassen, J. F. Collins, and D. G. Higgins, Finding flexible patterns in unaligned protein sequences. Protein Science, 4(8), 1995.
  25. R. Kohavi, C. Brodley, B. Frasca, L. Mason, and Z. Zheng, KDD-cup 2000 organizers' report: Peeling the Onion. SIGKDD Explorations, 2, 2000.
  26. Jianyong Wang, Jiawei Han: BIDE: Efficient Mining of Frequent Closed Sequences. ICDE 2004: 79-90
  27. Anurag Choubey, Dr. Ravindra Patel and,Dr. J. L. Rana. Article: Frequent Pattern Mining With Closeness Considerations: Current State Of The Art. GJCST Issue 11, Volume 17, August 2011. Published by Global Journals, 25200 Carlos Bee Blvd. #495, Hayward, CA 94542, USA Published by Foundation of Computer Science, New York
  28. R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD, pp. 207-216, 1993.
  29. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
  30. A. Silberschatz and A. Tuzhilin, "What Makes Patterns Interesting in Knowledge Discovery Systems," IEEE Trans. Knowledge and Data Eng. vol. 8, no. 6, pp. 970-974, Dec. 1996.
  31. M. J. Zaki and M. Ogihara, "Theoretical Foundations of Association Rules," Proc. Workshop Research Issues in Data Mining and Knowledge Discovery (DMKD '98), pp. 1-8, June 1998.
  32. D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, and T. Yiu, "Mafia: A Maximal Frequent Itemset Algorithm," IEEE Trans. Knowledge and Data Eng. , vol. 17, no. 11, pp. 1490-1504, Nov. 2005.
  33. J. Li, "On Optimal Rule Discovery," IEEE Trans. Knowledge and Data Eng. , vol. 18, no. 4, pp. 460-471, Apr. 2006.
  34. M. J. Zaki, "Generating Non-Redundant Association Rules," Proc. Int'l Conf. Knowledge Discovery and Data Mining, pp. 34-43, 2000.
  35. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, "Efficient Mining of Association Rules Using Closed Itemset Lattices," Information Systems, vol. 24, pp. 25-46, 1999.
  36. H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hatonen, and H. Mannila, "Pruning and Grouping of Discovered Association Rules," Proc. ECML-95 Workshop Statistics, Machine Learning, and Knowledge Discovery in Databases, pp. 47-52, 1995.
  37. B. Baesens, S. Viaene, and J. Vanthienen, "Post-Processing of Association Rules," Proc. Workshop Post-Processing in Machine Learning and Data Mining: Interpretation, Visualization, Integration, and Related Topics with Sixth ACM SIGKDD, pp. 20-23, 2000.
  38. http://archive. ics. uci. edu/ml/datasets/
  39. Anurag Choubey, Dr. Ravindra Patel, Dr. J. L. Rana "Concurrent Edge Prevision and Rear Edge Pruning Approach for Frequent Closed Itemset Mining", IJACSA, Volume 2 No. 11, November 2011
Index Terms

Computer Science
Information Sciences

Keywords

Counter Support Measurement Csm Concurrent Edge Prevision And Rear Edge Pruning Ceg&rep Gazelle

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