CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art

by A Muralidhar, V. Pattabiraman
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 22
Year of Publication: 2013
Authors: A Muralidhar, V. Pattabiraman
10.5120/11531-7391

A Muralidhar, V. Pattabiraman . Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art. International Journal of Computer Applications. 67, 22 ( April 2013), 43-51. DOI=10.5120/11531-7391

@article{ 10.5120/11531-7391,
author = { A Muralidhar, V. Pattabiraman },
title = { Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 22 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 43-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number22/11531-7391/ },
doi = { 10.5120/11531-7391 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:26:11.416721+05:30
%A A Muralidhar
%A V. Pattabiraman
%T Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 22
%P 43-51
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identifying the association rules in colossal datasets is possessing elevated level of presence in data mining or data exploration. As a consequence, countless algorithms are approximated to deal alongside this issue. The two setbacks ambitious considering this outlook are: ascertaining all frequent item sets and to produce limits from them. This document is for the most portions aimed at pondering of past scrutiny, present useful rank and to ascertain the gaps of them alongside present ambitious information. Here, early subject, as it acquires extra time to process, is computationally expensive. Current discover targeted on these algorithms and their connected issues.

References
  1. Agrawal, R. , T, Imielinski and A, Swami, 1993, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, May 25-28, ACM, New York, USA. , pp: 207-216
  2. Agrawal, R, and R, Srikant, 1994, Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very Large Data Bases, Sept, 12-15, San Francisco, CA. , USA. , pp: 487-499
  3. Mannila, H. , H, Toivonen and A, Inkeri Verkamo, 1994 Efficient algorithms for discovering association rules Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, (KDD-94), IEEE, pp: 181-192
  4. Han, J. , J, Pei, Y, Yin and R, Mao, 2004, Mining frequent patterns without candidate generation: A frequent-pattern tree approach, Data Mining Knowledge Discovery, 8: 53-87
  5. Savasere, A. , E, Omieccinski and S, Navathe, 1995, An efficient algorithm for mining association rules in large databases, Proceedings of the 21st International Conference on Very Large Databases, Sept, 11-15, Zurich, Switzerland, pp: 432-443
  6. Toivonen, H. , 1996, Sampling large databases for association rules, Proceedings of 22th International Conference on Very Large Databases, Sept, 3-6, Bombay, India, pp: 134-145
  7. Brin, S. , R, Motwani and C, Silverstein, 1997, Beyond market basket: Generalizing association rules to correlations, Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, May 11-15, Tucson, AZ. , pp: 265-276
  8. Hidber, C. , 1999, Online association rule mining, ACM SIGMOD Rec. , 28: 145-156
  9. B. Liu, W. Hsu, and Y. Ma, "Mining association rules with multiple minimum supports,", Proceedings of the fifth ACM SIGKDD international conference, San Diego, CA, USA August 15-18, 1999, p. 341
  10. . Ezeife, C. I. ; Min Chen; Incremental mining of Web sequential patterns using PLWAP tree on tolerance MinSupport, Database Engineering and Applications Symposium, 2004, Issue Date: 7-9 July 2004, On page(s): 465 - 469
  11. Pei, J. , J, Han and L,V,S, Lakshmanan, 2001, Mining frequent itemsets with convertible constraints, Proceedings of the 17th International Conference on Data Engineering, April 2-6, Heidelberg, Germany, pp: 433-332
  12. Liu, J. , Y, Pan, K, Wang and J, Han, 2002, Mining frequent item sets by opportunistic projection, Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery in Databases, July 23-26, Edmonton, Canada, pp: 239-248
  13. Grahne, G, and J, Zhu, 2003, Efficiently using prefix-trees in mining frequent itemsets, Proceedings of the 2003 ICDM International Workshop on Frequent Itemset Mining Implementations, (IWFIMI03), Melbourne, FL. , pp: 123-132
  14. Lakshmanan, L,V,S. , R, Ng, J, Han and A, Pang, 1999, Optimization of constrained frequent set queries with 2-variable constraints, ACM SIGMOD Rec. , 28: 157-168
  15. Grahne, G. , L, Lakshmanan and X, Wang, 2000, Efficient mining of constrained correlated sets, Proceedings of the 2000 International Conference on Data Engineering, Feb, 28-March 3, San Diego, CA. , pp: 512-521
  16. Bucila, C. , J, Gehrke, D, Kifer and W, White, 2003, DualMiner: A dual-pruning algorithm for itemsets with constraints, Data Min, Knowl, Discov. , 7: 241-272
  17. Bonchi, F. , F, Giannotti, A, Mazzanti and D, Pedreschi, 2003, Exante: Anticipated data reduction in constrained pattern mining, Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Sept, 22-26, Cavtat, Dubrovnik, Croatia, pp: 59-70
  18. Gade, K. , J, Wang and G, Karypis, 2004, Efficient closed pattern mining in the presence of tough block constraints, Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug, 22-25, Seattle, WA. , pp: 138-147
  19. Bonchi, F, and C, Lucchese, 2004, On closed constrained frequent pattern mining, Proceedings of the 2004 International Conference on Data Mining, Nov, 1-4, Brighton, UK. , pp: 35-42
  20. Yun, U, and J, Leggett, 2005, Wfim: Weighted frequent itemset mining with a weight range and a minimum weight, Proceedings of the 2005 SIAM International Conference on Data Mining, April 21-23, Newport Beach, CA. , pp: 636-640
  21. Ya-Han Hu; Fan Wu; Tzu-Wei Yen "Considering RFM-values of frequent patterns in transactional databases", 2nd International Conference on Software Engineering and Data Mining (SEDM), June 2010, pages: 422 - 427
  22. Long, Z. A. Hamdan, A. R. Bakar, A. A; Parameter setting procedure via quick parameter evaluation in frequent pattern mining for outbreak detection, 2nd Conference on Data Mining and Optimization, 2009. DMO '09, Issue Date: 27-28 Oct. 2009, On page(s): 90 - 93
  23. Antunes, C. ; Pattern Mining over Star Schemas in the Onto4AR Framework, IEEE International Conference on Data Mining Workshops, 2009, ICDMW '09, Issue Date: 6-6 Dec. 2009, On page(s): 453 - 458
  24. Ya-Han Hu; Fan Wu; Yi-Chun Liao; Sequential pattern mining with multiple minimum supports: A tree based approach, 2nd International Conference on Software Engineering and Data Mining (SEDM), Issue Date: 23-25 June 2010 On page(s): 428 – 433
  25. Chuang-Kai Chiou, Judy C. R. Tseng; Sorted Compressed Tree: An Improve Method of Frequent Patterns Mining without Support Constraint, 2nd International Conference on Software Engineering and Data Mining (SEDM), 2010, Issue Date: 23-25 June 2010, On page(s): 328 - 333
  26. Agarwal, R,C. , C, Aggarwal and V,V,V, Prasad, 2001, A tree projection algorithm for generation of frequent item sets, J, Parallel Distributed Comput. , 61: 350-371
  27. . Yao, H. , Hamilton, H. J. , Butz, C. J. : A Foundational Approach to Mining Itemset Utilities from Databases. In: Third SIAM Int. Conf. on Data Mining, pp. 482–486 (2004)
  28. . Yao, H. , Hamilton, H. J. : Mining itemset utilities from transaction databases. Data & Knowledge Engineering 59, 603–626 (2006)
  29. . Liu, Y. , Liao, W. -K. , Choudhary, A. : A Two Phase algorithm for fast discovery of High Utility of Itemsets. In: Ho, T. -B. , Cheung, D. , Liu, H. (eds. ) PAKDD 2005. LNCS(LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005)
  30. . Erwin, A. , Gopalan, R. P. , Achuthan, N. R. : CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach. In: 7th IEEE Int. Conf. on Computer and Information Technology (CIT 2007), pp. 71–76 (2007)
  31. . Li, Y. -C. , Yeh, J. -S. , Chang, C. -C. : Isolated items discarding strategy for discovering high utility itemsets. Data & Knowledge Engineering 64, 198–217 (2008)
  32. . Tanbeer, S. K. , Ahmed, C. F. , Jeong, B. -S. , Lee, Y. -K. : CP-tree: A tree structure for single pass frequent pattern mining. In: Washio, T. , Suzuki, E. , Ting, K. M. , Inokuchi, A. (eds. ) PAKDD 2008. LNCS(LNAI), vol. 5012, pp. 1022–1027. Springer, Heidelberg (2008)
  33. F. Wu, Y. -S. Lee, and J. -N. Yu, "An adaptive approach for modelselection with high stability," in Proceedings of International JointConference on e-Commerce, e-Administration, e-Society, and e-Education, Bangkok, Thailand, 2008.
  34. Y. -L. Chen, M. -H. Kuo, S. -Y. Wu, and K. Tang, "Discovering recency,frequency, and monetary (RFM) sequential patterns from customers'purchasing data," Electronic Commerce Research and Applications, vol. 8, pp. 241-251, 2009.
  35. J. Han, J. Pei, Y. Yin, and R. Mao, "Mining frequent patterns withoutcandidate generation: A frequent-pattern tree approach," Data Miningand Knowledge Discovery, vol. 8, pp. 53-87, 2004.
  36. S. Zhang, et al. , "Computing the minimum-support for mining frequent patterns," Knowledge and Information Systems, vol. 15, no. 2, 2008, pp. 233-257
  37. G. Ramesh, W. Maniatty, and M. J. Zaki. Feasible itemset distributions in data mining: theory and application. In Proceedings ACM PODS'03, pages 284–295, 2003.
  38. L. Lhote, F. Rioult, and A. Soulet. Average number of frequent (closed) patterns in bernouilli and markovian databases. In Proceedings IEEE ICDM'05, pages 713–716, 2005.
  39. F. Geerts, B. Goethals, and J. V. den Bussche. Tight upper bounds on the number of candidate patterns. ACM Trans. on Database Systems, 30(2):333–363, 2005.
  40. U. Keich and P. A. Pevzner. Subtle motifs: defining the limits of motif finding algorithms. Bioinformatics, 18(10):1382–1390, 2002.
  41. J. Besson, et al. , "Parameter Tuning for Differential Mining of String Patterns," IEEE Computer Society Washington, DC, USA, 2008, pp. 77-86
  42. M. Boley, et al. , "A Randomized Approach for Approximating the Number of Frequent Sets," IEEE Computer Society Washington, DC, USA, 2008, pp. 43-52.
  43. Valiant, L. G. : The complexity of computing the permanent. Theor. Comput. Sci. 8, 189–201 (1979)
  44. . Gunopulos, D. , Khardon, R. , Mannila, H. , Saluja, S. , Toivonen, H. , Sharm, R. S. : Discovering all most specific sentences. ACM Trans. Database Syst. 28(2), 140–174 (2003)
  45. . Jerrum, M. , Sinclair, A. : Approximating the permanent. SIAM J. Comput. 18(6), 1149–1178 (1989)
  46. B. Liu, W. Hsu, and Y. Ma, "Mining association rules with multiple minimum supports," in Proceedings of the 1999 International Conference on Knowledge Discovery and Data Mining, pp. 337-341, 1999.
  47. Y. Lee, T. Hong, and W. Lin, "Mining association rules with multiple minimum supports using maximum constraints," International Journal of Approximate Reasoning, vol. 40, pp. 44-54, 2005.
  48. Y. C. Lee, T. P. Hong and W. Y. Lin, "Mining fuzzy association rules with multiple minimum supports using maximum constraints", The Eighth International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 2004, Lecture Notes in Computer Science, Vol. 3214, pp. 1283-1290, 2004.
  49. Y. C. Lee, T. P. Hong and W. Y. Lin, "Mining association rules with multiple minimum supports using maximum constraints," International Journal of Approximate Reasoning, Vol. 40, No. 1, pp. 44-54, 2005.
  50. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: Proceedings of the 19th ACM SIGMOD International Conference on Management of Data, 2000, pp. 1–12.
  51. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, D. Yang, H-mine: hyper-structure mining of frequent patterns in large database, in: Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, 2001, pp. 441–448.
  52. M. EI-Hajj, O. R. Zaiane, Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining, in: The ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 109–118.
  53. G. Liu, H. Lu, J. X. Yu, CFP-tree: a compact disk-based structure for storing and querying frequent itemsets, Information Sciences 32 (2007) 295–319.
  54. T. Hu, S. Y. Sung, H. Xiong, Q. Fu, Discovery of maximum length frequent itemsets, Information Sciences 178 (2008) 68–87.
  55. W. Cheung and O. R. Zaiane, "Incremental mining of frequent patterns without candidate generation or support constraint," Citeseer, 2003, pp. 111-116.
  56. S. Y. Liu, "An Efficiency Incremental Mining with Grouping Compress Tree," Unpublished master's thesis, National Central University Taoyuan Country, Taiwan, 2004
Index Terms

Computer Science
Information Sciences

Keywords

Frequent Itemset Mining utility mining Frequent pattern mining Association rules Data mining

Powered by PhDFocusTM