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A Comparative Analysis of NFA and Tree-based approach for Infrequent Itemset Mining

Authors(2):

Kalaiyarasi. P, Prof. Manikandan. M
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Frequent itemset mining is an exploratory data mining technique widely used for discovering valuable correlations among data. Frequent itemset mining is a core component of data mining and variations of association analysis, like association rule mining. Extraction of frequent itemsets is a core step in many association analysis techniques. The frequent occurrence of item is expressed in terms of the support count. An item is said to be frequent whose number of occurrences is greater than threshold value. Recently, Infrequent Item sets are also considered to be important in various situations. Infrequent Item set mining is the just the variation of frequent item set mining, where it takes the rarely occurred items in the database. The use of infrequent item set mining is to help for the decision making systems. There are several existing algorithms like Apriori, F-Miner, FP-growth, Residual tree algorithm, Fast algorithm to mine the frequent item sets which takes more computational time. The proposed system is to mine the infrequent item sets by mathematical modeling technique(NFA)where, results in less computing time.

Kalaiyarasi. P, Prof. Manikandan. M

Association rule mining (ARM), Itemset mining, Frequent itemsets, Frequent patterns, Infrequent Items, Non-deterministic finite automata(NFA).

' [1] Agrawal , R. , Imieliński , T. , & Swami , A.”Mining association rules between sets of items in large databases”. In proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington, DC, 1993.
[2] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int’l Conf. Very Large Data Bases (VLDB ’94), pp. 487-499, 1994.
[3] Luca Cagliero and Paolo Garza “Infrequent Weighted Itemset Mining using Frequent Pattern Growth”, IEEE Transactions on Knowledge and Data Engineering, pp. 1- 14, 2013
[4] Liu,G. , Lu ,H. , Yu ,J. X., Wang, W., & Xiao, X.. ”AFOPT:An Efficient Implementation of Pattern Growth Approach”, In Proc. IEEE ICDM'03 Workshop FIMI'03, 2003
[5] J.Han, M.Kamber. Data Ming Concepts and Techniques, Second Edition. Morgan Kaufmann Publisher, Aug. 2000.
[6] G.Grahne, J.Zhu. Efficiently Using Prefix-trees in Mining Frequent Itemsets. In ICDM'03, 2003.
[7] G.Liu, H.Lu, W.Lou, et al. Efficient Mining of Frequent Patterns Using Ascending Frequency Ordered Prefix-Tree. In DASFAA, 2003.
[8] Frans Coenen, Paul Leng & Shakil Ahmed, 2004 ‘Data Structure for Association Rule Mining: T-Trees and P-Trees’, IEEE Transactions on Knowledge and Data Egineering, vol.16, no.6.








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Publication Details

Published in : Volume 1 | Issue 1 | January-Febuary - 2015
Date of Publication Print ISSN Online ISSN
2015-02-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
108-112 IJSRSET151116   Technoscience Academy

Cite This Article

Kalaiyarasi. P, Prof. Manikandan. M, "A Comparative Analysis of NFA and Tree-based approach for Infrequent Itemset Mining", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 1, pp.108-112, January-Febuary-2015.
URL : http://ijsrset.com/IJSRSET151116.php