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Reseach Article

A Survey on Frequent Itemset Mining with Association Rules

by Endu Duneja, A.k. Sachan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 23
Year of Publication: 2012
Authors: Endu Duneja, A.k. Sachan
10.5120/7105-9720

Endu Duneja, A.k. Sachan . A Survey on Frequent Itemset Mining with Association Rules. International Journal of Computer Applications. 46, 23 ( May 2012), 18-24. DOI=10.5120/7105-9720

@article{ 10.5120/7105-9720,
author = { Endu Duneja, A.k. Sachan },
title = { A Survey on Frequent Itemset Mining with Association Rules },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 23 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number23/7105-9720/ },
doi = { 10.5120/7105-9720 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:24.691413+05:30
%A Endu Duneja
%A A.k. Sachan
%T A Survey on Frequent Itemset Mining with Association Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 23
%P 18-24
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining techniques comprises of Clustering, Association, Sequential mining, Classification, Regression and Deviation detection Association Rule mining is one of the utmost ubiquitous data mining techniques which can be defined as extracting the interesting correlation and relation among huge amount of transactions. Many applications engender colossal amount of operational and behavioral data. Copious effective algorithms are proposed in the literature for mining frequent itemsets and association rules. Integrating efficacy considerations in data mining tasks is reaping popularity in recent years. Business value is enhanced by certain association rules and the data mining community has acknowledged the mining of these rules of interest since a long time. The discovery of frequent itemsets and association rules from transaction databases has aided many business applications. To discover the concealed knowledge from these data association rule mining can be applied in any application. A comprehensive analysis, survey and study of various approaches in existence for frequent itemset extraction, association rule mining with efficacy contemplations have been presented in this paper.

References
  1. Jiawei Han & Michelien Kamber, Edition 2003. "Data Mining: Concepts & Techniques", published by Elsevier, ISBN: 81-8147-049-4, pp 226-230.
  2. Yu-Chiang Li, Jieh-Shan Yeh, Chin-Chen Chang, 2005. "Efficient Algorithms for Mining Share-Frequent Itemsets", In Proceedings of the 11th World Congress of Intl. Fuzzy Systems Association.
  3. R. Agrawal, T. Imielinski, and A. Swami, 1993. "Mining association rules between sets of items in large databases", in proceedings of the ACM SIGMOD Int'l Conf. on Management of data, pp. 207-216.
  4. S. Kotsiantis, D. Kanellopoulos, 2006. "Association Rules Mining: A Recent Overview", GESTS International Transactions on Computer Science and Engineering, Vol. 32, No. 1, pp. 71-82.
  5. R. Agrawal, T. Imielinksi and A. Swami, Database Mining: a performance perspective, IEE Transactions on knowledge and Data Engineering, 1993.
  6. M. S. Chen, J. Han and P. S. Yu. , 1996. "Data Mining : An overview from a database perspective", IEE Transactions on Knowledge and Data Engineering.
  7. C. Y. Wang, T. P. Hong and S. S. Tseng, 2002. "Maintenance of discovered sequential patterns for record deletion" in Intell. Data Anal. , pp. 399-410.
  8. R. Agrawal, T. Imielinski, and A. Sawmi, 1993. "Mining association rules between sets of items in large databases", In proc. of the ACM SIGMOD Conference on Management of Data, pp. 207-216.
  9. R. Agrawal and R. Srikant. 1994. "Fast algorithms for mining association rules", In Proc. of Intl. Conf. On Very Large Databases (VLDB), pp. 487-499.
  10. S. Brin, R. Motwani, J. D. Vllman, and S. Tsur, 1997. "Dynamic itemset counting and implication rules for market basket data", SIGMOD Record (ACM Special Interest Group on Management of Data), vol. 26(2), pp. 255.
  11. H. Toivonen, 1996. "Sampling large databases for association rules", In the VLDB Journal, pp. 134-145.
  12. A. Sarasere, E. Omiecinsky,and S. Navathe, 1995. "An efficient algorithm for mining association rules in large databases" In Proc. 21St International Conference on Very Large Databases (VLDB) , Zurich, Switzerland, Technical Report No. GIT-CC-95-04.
  13. Y. F. Jiawei Han. , 1995. "Discovery of multiple-level association rules from large databases" In Proc. of the 21St International Conference on Very Large Databases (VLDB), Zurich, Switzerland.
  14. Y. Fu. , 1996. "Discovery of multiple-level rules from large databases".
  15. D. W. L. Cheung, J. Han and C. Y. Wong. , 1996. "Maintenance of discovered association rules in large databases: An incremental updating technique" In ICDE, pp. 106-114.
  16. D. W. L. Cheung, S. D. Lee, and B. Kao. , 1997. "A general incremental technique for maintaining discovered association rules" In Database Systems for advanced Applications, pp. 185-194.
  17. S. Thomas, S. Bodadola, K. Alsabti, and S. Ranka, 1997. "An efficient algorithm for incremental updation of association rules in large databases" In Proc. KDD'97, pp. 263-266.
  18. R. Agrawal and R. Srikant, 1995. "Mining sequential patterns" In P. S. Yu and A. L. P. Chen, editors, Proc. 11the Int. Conf. Data engineering. ICDE, pp. 3-14. IEEE pp, 6-10.
  19. R. Agrawal, T. Imielinski, and A. Swami, 1993. "Mining association rules between sets of items in large databases", in proceedings of the ACM SIGMOD Int'l Conf. on Management of data, pp. 207-216.
  20. M. Houtsma, and Arun Swami, 1995. "Set-Oriented Mining for Association Rules in Relational Databases". IEEE International Conference on Data Engineering, pp. 25–33.
  21. Rakesh Agrawal, and Ramakrishnan Srikant, 1994. "Fast Algorithms for Mining Association Rules", In Proceedings of the 20th Int. Conf. Very Large Data Bases, pp. 487-499.
  22. Srikant R, Agrawal R. , 1995. "Mining generalized association rules". In: Dayal U, Gray P M D, Nishio Seds. Proceedings of the International Conference on Very Large Databases. San Francisco, CA: Morgan Kanfman Press, pp. 406-419
  23. J. S. Park, M. -S. Chen, and P. S. Yu, 1995. "An effective hash based algorithm for mining association rules", In Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, vol. 24(2) of SIGMOD Record, pp. 175–186.
  24. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, 1996. "Fast discovery of association rules" In Advances in Knowledge Discovery and Data Mining, MIT Press, pp. 307–328.
  25. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, 1997. "Dynamic itemset counting and implication rules for market basket data". In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, vol. 26(2), pp. 255–264.
  26. C. Hidber, 1999. "Online association rule mining". In Proc. of the 1999 ACM SIGMOD International Conference on Management of Data, vol. 28(2), pp. 145–156.
  27. M. J. Zaki and C. -J. Hsiao, 1999. "CHARM: An efficient algorithm for closed association rule mining". Computer Science Dept. , Rensselaer Polytechnic Institute, Technical Report pp. 99-10,
  28. M. J. Zaki, May/June 2000. "Scalable algorithms for association mining". IEEE Transactions on Knowledge and Data Engineering, vol. 12(3), pp. 372–390.
  29. Wei Wang, jiong yang and philip S. Yu, 2000. "Efficient Mining of Weighted Association Rules (WAR)", Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 270 - 274.
  30. R. Agrawal, C. Aggarwal, and V. Prasad, 2000. "A Tree Projection Algorithm for Generation of Frequent Item Sets", Parallel and Distributed Computing, pp. 350-371.
  31. G. Grahne and J. Zhu, 2003. "Efficiently Using Prefix-Trees in Mining Frequent Itemsets", In proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI).
  32. Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao, 2004. "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, vol. 8, issue 1, pp. 53 – 87.
  33. G. S. Manku and R. Motwani, 2002. "Approximate Frequency Counts Over Data Streams". In Proc. of the 28th VLDB(Very Large Data Bases) conference, pp. 346–357.
  34. M. J. Zaki and K. Gouda, 2003. "Fast Vertical Mining Using Diffsets", Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 326-335.
  35. F. Bodon, 2003. "A Fast Apriori Implementation", In Proc. of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, vol. 90.
  36. Liang Dong and Christos Tjortjis, 2003. "Experiences of Using a Quantitative Approach for Mining Association Rules", in Lecture Notes Computer Science, vol. 2690, pp. 693-700.
  37. Hua-Fu Li, Suh-Yin Lee and Man-Kwan Shan, 2004. "An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams", In Proc. of the 1st Int'l. Workshop on Knowledge Discovery in Data Streams, pp. 20- 24.
  38. Chuan Wang, Christos Tjortjis, 2004. "PRICES: An efficient algorithm for mining association rules", in Lecture Notes Computer Science vol. 3177, pp. 352-358, ISSN: 0302-9743.
  39. M. H. Margahny & A. A. Mitwaly, 2005. "Fast algorithm for mining association rules", AIML Conference, CICC, Cairo, Egypt.
  40. Mingjun Song and Sanguthevar Rajasekaran, 2006. "A Transaction Mapping Algorithm for Frequent Itemsets Mining", IEEE Transactions on Knowledge and Data Engineering, vol. 18(4), pp. 472-481.
  41. Yanbin Ye and Chia-Chu Chiang, 2006. "A Parallel Apriori Algorithm for Frequent Itemsets Mining", Fourth International Conference on Software Engineering Research, Management and Applications, pp. 87- 94.
  42. M. Sulaiman Khan, Maybin Muyeba, Christos Tjortjis, Frans Coenen, 2006. "An effective Fuzzy Healthy Association Rule Mining Algorithm (FHARM)", In Lecture Notes Computer Science, vol. 4224, pp. 1014-1022, ISSN: 0302-9743.
  43. M. Sulaiman Khan, Maybin Muyeba, Frans Coenen, 2008. "Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework", in ALSIP (PAKDD), pp. 52-64.
  44. M. Hahsler, C. Buchta and K. Hornik, 2007. "Selective Association Rule Generation", in Proceedings on Computational Statics, Springer.
  45. Kamrul, Shah, Mohammad, Khandakar, Hasnain, Abu, 2008. "Reverse Apriori Algorithm for Frequent Pattern Mining", Asian Journal of Information Technology, pp. 524-530, ISSN: 1682-3915.
  46. E. Ansari, G. H. Dastghaibfard, M. Keshtkaran, H. Kaabi 2008. "Distributed Frequent Itemset Mining using Trie Data Structure", IAENG, vol. 35:3.
  47. S. Praksh, R. M. S. Parvathi 2010. "An enhanced Scalling Apriori for Association Rule Mining Efficiency", European Journal of Scientific Research, vol. 39, pp. 257-264, ISSN: 1450-216X.
  48. S. Rao, P. Gupta 2012. "Implementing improved algorithm over Apriori data mining association rule algorithm", IJCST, vol. 3, pp. 489-493, ISSN: 2229-4333.
  49. Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, December 2000. "Mining frequent patterns with counting inference", SIGKDD Explorations, vol. 2(2).
  50. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, January 1999. "Discovering frequent closed itemsets for association rules", In 7th Intl. Conf. on Database Theory.
  51. J. Pei, J. Han, and R. Mao,May 2000. "Closet: An efficient algorithm for mining frequent closed itemsets", In SIGMOD Int'l Workshop on Data Mining and Knowledge
Index Terms

Computer Science
Information Sciences

Keywords

Frequent Itemset Association Rule