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A Recent Review on Itemset Tree Mining: MEIT Technique

International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 113 - Number 17
Year of Publication: 2015
Tanvi P. Patel
Warish D. Patel

Tanvi P Patel and Warish D Patel. Article: A Recent Review on Itemset Tree Mining: MEIT Technique. International Journal of Computer Applications 113(17):39-42, March 2015. Full text available. BibTeX

	author = {Tanvi P. Patel and Warish D. Patel},
	title = {Article: A Recent Review on Itemset Tree Mining: MEIT Technique},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {113},
	number = {17},
	pages = {39-42},
	month = {March},
	note = {Full text available}


Association rule mining forms the core of data mining and it is termed as one of the well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, Association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. This paper provides the preliminaries of basic concepts about Itemset mining and survey the list of existing tree structure algorithms. These algorithms include various tasks such as fast query processing, optimizing memory space and reducing tree construction time. For mining maximal frequent pattern various algorithms used which optimization the search space for pruning.


  • P. Fournier-Viger, E. Mwamikazi, T. Gueniche, U. Faghihi, "MEIT: Memory Efficient Item-set Tree for Targeted Association Rule Mining", 9th International Conference, ADMA 2013, Part II, Volume 8347 - Springer, Heidelberg, pp. 95-106, 2013.
  • M. Kubat, A. Hafez, V. V. Raghavan, J. R. Lekkala, W. K. Chen, "Item-set trees for targeted association querying", Knowledge and Data Engineering, IEEE Transactions on Volume 15( 6 ), pp. 1522 – 1534, 2003.
  • A. Hafez, J. Deogun, V. V. Raghavan, "The Item-Set Tree: A Data Structure for Data Mining", First International Conference, DaWaK'99 Florence, Italy, August 30 – September - Springer, pp. 183-192, 1999.
  • R. Agrawal, T. Imielinski and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM - SIGMOD, pp. 207-216, 1993.
  • P. Fournier-Viger, C. W. Wu, V. S. Tseng, "Mining Top-K Association Rules", L. Kosseim, D. Inkpen, Canadian AI 2012. LNCS, vol. 7310, Springer- Heidelberg, pp. 61–73, 2012.
  • P. Fournier-Viger, V. S. Tseng, "Mining Top-K Non-Redundant Association Rules" Chen, L. , Felfernig, A. , Liu, J. , Ras, Z. W. (eds. ) ISMIS 2012. LNCS, vol. 7661 - Springer, Heidelberg, Pp. 31–40, 2012.
  • S. Dandu, B. L. Deekshatulu, "Improved Algorithm for Frequent Item sets Mining Based on Apriori and FP-Tree", Global Journal of Computer Science and Technology, Global Journal of Computer Science and Technology, Volume 13, 2013.
  • C. K. Leung, Q. I. Khan, Z. Li, T. Hoque "CanTree: a canonical-order tree for incremental frequent-pattern mining", Knowledge and Information Systems – Springer, pp. 287-311, April 2007.
  • D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, T. Yiu, "MAFIA: A Maximal Frequent Itemset Algorithm", IEEE transactions on knowledge and data engineering, vol. 17, no. 11, November 2005.
  • K. Gouda, M. J. Zaki, "GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets", Data Mining and Knowledge Discovery - Springer, Volume 11, Issue 3, pp. 223-242, November 2005.
  • C. I. Ezeife, Y. Su, "Mining incremental association rules with generalized FP-tree", 15th Conference of the Canadian Society for Computational Studies of Intelligence, Volume 2338 - Springer, Heidelberg, pp 147-160, 2002.
  • M. J. Zaki, K. Gouda, "Fast vertical mining using diffsets", Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - ACM Press, Pp. 326–335, 2003.
  • J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, D. Yang, "H-Mine: Fast and space-preserving frequent pattern mining in large databases", IIE Transactions, Volume 39(6), Pp. 593-605, 2007.
  • J. H. Chang, W. S. Lee, "Finding Recent Frequent Itemsets Adaptively over Online Data Streams", Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, SIGKDD, Pp. 487-492, August 24-27, 2003.
  • S. Kotsiantis, D. Kanellopoulos, "Association Rules Mining: A Recent Overview", GESTS International Transactions on Computer Science and Engineering, Vol. 32 (1), pp. 71-82, 2006.
  • F. M. Christian, N. C. Chauhan, N. B. Prajapati, "A Comparative Study of Frequent Pattern Recognization Techniques from Stream Data", International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2(1), January 2014.
  • P. Fournier-Viger, U. Faghihi, R. Nkambou, E. M. Nguifo "CMRules: Mining Sequential Rules Common to Several Sequences", Volume 25(1), Pp. 63–76 , Elsevier - February 2012.
  • K. Lai, N. Cerpa, "Support vs Confidence in Association Rule Algorithms", Proceedings of the OPTIMA Conference, Curico, 2001.
  • F. Coenen, G. Goulbourne, P. Leng, "Tree Structures for Mining Association Rules", Data Mining and Knowledge Discovery, Kluwer Academic Publishers, volume 8, Pp. 25–51, 2004.
  • Y. H. Hua, Y. L. Chen, "Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism", Volume 42(1), Pp. 1–24, October 2006.
  • T. Gueniche, P. Fournier-Viger, V. Tseng, "Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction", 9th International Conference, ADMA, Part II, vol. 8347, Springer, Heidelberg, pp. 177–188, 2013.
  • P. Fournier-Viger, A. Gomariz, T. Gueniche, E. Mwamikazi, R. Thomas, "TKS: Efficient Mining of Top-K Sequential Patterns", 9th International Conference, China, Part I, Volume 8346, Springer Heidelberg, pp. 109-120, December 14-16, 2013.
  • K. Gouda, M. J. Zaki, "Efficiently Mining Maximal Frequent Itemsets," Proc. First IEEE International Conference of Data Mining, Pp. 163 – 170, November 2001.
  • J. Han, M. Kamber, J. Pei, "Data Mining: Concepts and Techniques", 2nd edition, Morgan Kaufmann, San Francisco, 2006.