Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

A Novelty Approach for Finding Frequent Itemsets in Horizontal and Vertical Layout- HVCFPMINETREE

Print
PDF
 
Download Full Text
International Journal of Computer Applications
© 2010 by IJCA Journal
Number 5 - Article 4
Year of Publication: 2010
Authors:
A.Meenakshi
Dr.K.Alagarsamy
10.5120/1478-1995

A.Meenakshi and Dr.K.Alagarsamy. Article:A Novelty Approach for Finding Frequent Itemsets in Horizontal and Vertical Layout- HVCFPMINETREE. International Journal of Computer Applications 10(5):20–27, November 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {A.Meenakshi and Dr.K.Alagarsamy},
	title = {Article:A Novelty Approach for Finding Frequent Itemsets in Horizontal and Vertical Layout- HVCFPMINETREE},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {10},
	number = {5},
	pages = {20--27},
	month = {November},
	note = {Published By Foundation of Computer Science}
}

Abstract

In the modern world, we are faced with influx of massive data. Though such trend is most welcome, it poses a challenge to space-time requirement. So the imperative need is to find more efficient algorithms to manage such problem. There are so many existing algorithms to find frequent itemsets in Association Rule Mining. In this paper, we have modified FPTree algorithm as HVCFPMINETREE (Horizontal and vertical Compact Frequent Itemset Pattern Mining Tree). HVCFPMineTree combines all the maximum occurrence of frequent itemsets before converting into the tree structure. We have explained it with algorithm and illustrated with examples in horizontal data format and vertical data format

Reference

  • R.Agrawal, T. Imielinski and A. Swami, “Mining association rules between sets of Items in large databases”, in proceedings of the ACM SIGMOD International conference on Management of data, pp. 207-216, 1993.
  • R. Agarwal and R. Srikant, “Fast Algorithms for Mining Association Rules”proc.20th International Conference on very large Databases, pp 487-499,1994.
  • R. Agarwal, C. Aggarwal and V.V.V. Prasad: “A Tree projection Algorithm for Generation of Frequent Itemsets”. Journal of parallel and Distributed computing (Special issue on high performance data mining) 2000.
  • D. Burdick, M. Calimlin and J. Gehrke, “MAFIA: A Maximal frequent Itemset Algorithm for Transactional Databases,”Proc. International Conference on Data Engineering, PP 443-452, April 2001.
  • M.S. Chen, J. Han and P.S.YU,” Data Mining: An Overview from a Database Perspective”, IEEE Transaction on Knowledge Data Engineering 8(6), 866-897(1996).
  • J-Cios, W. Pedrycz and R. Swiniarski, Data Mining Methods for knowledge Discovery, Kluwer, Boston, 1998.
  • Erwin, A. Gopalan, R.P., and Achuthan, N.R., “CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach”, IEEE 7th International conferences on computer and Information Technology, pp 71-76, 2007.
  • B. Goethals, “Survey on Frequent Pattern Mining” manuscript, 2003
  • J. Han, J. Pei, Y. Yin and R. Mao, “Mining Frequent Pattern without Candidate Generation: A Frequent Pattern Tree”, Springer, volume 8, 53-87, 2004.
  • Jiawei Han et al, Data Mining: concepts and Techniques, Morgan Kaugmann publishers, 2001.
  • Jiawei Han, Jian pei, Yiwen Yin, Runying Mao, “Mining frequent patterns without candidate Generation: A frequent-pattern tree Approach” Data Mining and Knowledge Discovery, volume 8, Issue 1, pp 53-57 , January 2004..
  • B. Kalpana, Dr. R. Nadarajan, Optimizing Search Space Pruning in Frequent Itemset Mining with Hybrid Traversal strategies - A comparative performance on different data organizations, IAENG, 2007..
  • Laila A. Abd EI. Megid et al, “Vertical Mining of Frequent Patterns using Diffset Groups”, International. Conference on Intelligent systems Design and Applications.
  • Mohammed J. Zaki and Karam Gouda, “Fast Vertical Mining using Diffsets”, In proceedings of the ninth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, Washington, D.C, 326-335, 2003.
  • H. Mannila, Theoretical Frameworks for Data Mining SIGKDD Explorer 1(2), 30-32(2000)
  • Nittaya Kerdprasop and Kittisak Kerdprasop,Mining frequent patterns with functional programming, and, Internal Journal of computer and Information science and Engineering, 2007.
  • Pradeep Shenoy et al, Turbo-Charging Vertical Mining of large Databases , IISC, Technical report, DSL,2000.
  • A. Pietragaprina and D. Zandolin, “Mining Frequent Itemsets Using Patricia Tries”, proc. ICDM 2003 Workshop Frequent Itemsets Mining Implementations, December 2003.
  • M. Song and Rajasekaran , ”A Transaction Mapping Algorithm for Frequent Itemsets Mining”, IEEE transactions onknowledge and Data Engineering, vol 18, No. 4, 2006.
  • Tiwari.A, R.K.Gupta and Agrawal, “A Survey on Frequent Pattern Mining: Current Status and Challenging Issues”, Information Technology Journal, pp: 1278 – 1293, 2010.
  • M.J Zaki, C.J. Hsiao, “CHARM. An Efficient Algorithm for Closed Association Rule Mining,” Technical report 99-10, computer science department Resselaer polytechnic Institute, October 1993.
  • M.J. Zaki, S.Parthasarthy, M.Ogihara and W.Li. Parallel algorithm for discovery of association rules, Data Mining and Knowledge discovery, 1:343-374, 1997.
  • ZHENGXIN CHEN, Data Mining and Uncertain Reasoning: Integrated Approach, Wiley-Interscience Publications, 2001.