A Method for Reducing Repetitive Items on Weighted Data Using the Wit-wfi Algorithm

Volume 8, Issue 3, pp 282-296 http://dx.doi.org/10.22436/jmcs.08.03.11

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Authors

Akbar Rashidi - Department of Computer,Payame Noor University, PO BOX 19395-3697, Tehran, IRAN. Arash Ghorbannia Delavar - Department of Computer,Payame Noor University, PO BOX 19395-3697, Tehran, IRAN.

Abstract

Trying and mining frequent item sets plays an important role in the mining of association rules .in a dataset that stored with items and transactions an items can used for various significance .association rules is a important and considerable ways in data mining without presidency . one of discussion that today investigate is mining and finding frequent weighted item set and reduce run time of algorithm and reduce production frequent item sets is one of problem for research .at this paper we purpose present some method and ways for reduce run time of algorithm and reduce production frequent item set .all methods and ways applying on WIT algorithm and WIT-Tree structure .in first section we express and description classic association rule method (Apriori) and then WIT and then WIT-Diff algorithm and finally explain my proposed ways and experimental results.

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Keywords

• Data mining
• Frequent items
• Weighted item sets
• WIT-Tree
• Association rules.

MSC

•  68T05

References

• [1] B. Frans Coenen, Le. Bac , A new method for mining Frequent Weighted , , VLDB94 (2013), 489-499.
  • Google Scholar


• [2] R. Agrawal, R. Srikant , Fast algorithms for mining association rules, , VLDB’94 (1994), 487–499.
  • Google Scholar


• [3] C. H. Cai, A. W. Fu, C. H. Cheng, W. W. Kwong, Mining association rules, international database engineering , (1998), 68–77.
  • View Article
  • Google Scholar


• [4] T. P. Hong, Y. Y. Wu, S. L. Wang, An effective mining approach for up-todate, Expert Systems with Applications, (2009), 9747–9752.
  • View Article
  • Google Scholar


• [5] T. P. Hong, C. J. Wang, An efficient and effective association-rule, Expert Systems with, (2010), 618–626.


• [6] M. S. Khan, M. Muyeba, F. Coenen , A weighted utility framework, second uksim european symposium on, (2008), 87–92.
  • View Article
  • Google Scholar


• [7] B. Le, H. Nguyen, T. A. Cao, B. Vo , A novel algorithm for mining high utility, Asian conference on intelligent information and database, (2009), 13–16.
  • View Article
  • Google Scholar


• [8] B. Le, H. Nguyen, B. Vo, An efficient strategy for mining high utility, International Journal of Intelligent Information and Database Systems, (2011), 164–176.


• [9] , , http://fimi.cs.helsinki.fi/data/. Dataset. , ()
  • Google Scholar


• [10] B. Vo, T. P. Hong, B. Le, A dynamic bit-vector approach, Expert Systems with Applications, (2012), 7196–7206.
  • View Article
  • Google Scholar


• [11] B. Vo, B. L e, Interestingness measures for association rules, Expert Systems with Applications, (2011), 11630–11640.
  • View Article
  • Google Scholar


• [12] B. Vo, B. L e, Mining minimal non-redundant association rules, Journal of Intelligent Systems Technology and Applications, (2011), 92–106.
  • View Article
  • Google Scholar


• [13] C. H. Cai, A. W. Fu, C. H. Cheng, Mining association rules with weighted items, international database engineering , (1998), 68–77.
  • View Article
  • Google Scholar


• [14] B. Vo, Frans Coenen, Le. Bac , A new methid for mining frequent weighted itemsets based on wit-trees, , (2013)
  • View Article
  • Google Scholar


• [15] M. J. Zaki, Mining non-redundant association rules, Data Mining and Knowledge Discovery, 223–248 (2004)
  • View Article
  • MathSciNet
  • Google Scholar


• [16] Tao. F. Murtagh, Weighted association rule mining using weighted support and significance framework, Proceeding, 661-666 (2003)
  • View Article
  • Google Scholar


• [17] Russel Pears, Yun Sing Koh, Gillian Dobbie, Wai Yeap, Weighted association rule mining via a graph based connectivity model, Information Sciences, (2012)
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [18] Rouhollah Maghsoudi, Somayye Hoseini , Surveying Robot Routing Algorithms with Data Mining Approach, The Journal of Mathematics and Computer Science, Vol .2. (2011)
  • Google Scholar


• [19] Fatemeh Zabihi, Mojtaba Ramezan, Mir Mohsen Pedram, Azizollah Memariani, Rule Extraction for Blood Donators with Fuzzy Sequential Pattern Mining, The Journal of Mathematics and Computer Science, Vol. 2. (2011)
  • Google Scholar