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Reseach Article

An Incremental Approach for Mining Erasable Itemsets

by Suchi Shah, Jayna Shah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 15
Year of Publication: 2015
Authors: Suchi Shah, Jayna Shah
10.5120/21613-4854

Suchi Shah, Jayna Shah . An Incremental Approach for Mining Erasable Itemsets. International Journal of Computer Applications. 121, 15 ( July 2015), 1-6. DOI=10.5120/21613-4854

@article{ 10.5120/21613-4854,
author = { Suchi Shah, Jayna Shah },
title = { An Incremental Approach for Mining Erasable Itemsets },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 15 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number15/21613-4854/ },
doi = { 10.5120/21613-4854 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:28.805369+05:30
%A Suchi Shah
%A Jayna Shah
%T An Incremental Approach for Mining Erasable Itemsets
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 15
%P 1-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A factory has a production plan to produce products which are created from number of components and thus create profit. During financial crisis, the factory cannot afford to purchase all the necessary items as usual. Mining of erasable itemsets finds the itemsets which can be eliminated and do not greatly affect the factory's profit. The managers uses erasable itemset (EI) mining to locate EIs. If the manager wants to determine which new products are beneficial for the factory, we have to apply EI mining on the original database with new products from the scratch. So, here the incremental approach to mine erasable itemsets is proposed which scans only new products and update the EIs which were found previously from original database.

References
  1. J. Han, M. Kamber, and J. Pei, "Data mining: concepts and techniques" 3rd Edition, Morgan kaufmann, 2006. Deng, Z. , Fang, G. , Wang, Z. , Xu, X. , 2009. Mining erasable itemsets. In: ICMLC'09, pp. 67–73.
  2. Deng, Z. H. , Xu, X. R. , 2010. An ef?cient algorithm for mining erasable itemsets. In: ACDM'10, pp. 214–225.
  3. Deng, Z. H. , Xu, X. R. , 2012. Fast mining erasable itemsets using NC_sets. Expert Systems with Applications 39 (4), 4453–4463.
  4. Deng, Z. H. , 2013. Mining top-rank-k erasable itemsets by PID_lists. International Journal of Intelligent Systems 28 (4), 366–379.
  5. Le,T. , Vo,B. , Coenen,F. , 2013 An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. IEEE Copmuter society , pp. 2270-2274
  6. Le,T. , Vo,B. , 2013. MEI: An efficient algorithm for mining erasable itemsets . Elsevier'13, pp. 155-66
  7. Shweta,Garg,k. ,2013V Searching the best strategies of mining erasable itemsets. International Journal of scientific &engg. research (4), pp. 673–677.
  8. Le, T. P. , Vo, B. , Hong, T. P. , Le, B. , 2012. An ef?cient incremental mining approach based on IT-tree. In: IEEE.
  9. Aggarwal, C. C. , Li, Y. , Wang, J. , Wang, J. , 2009. Frequent pattern mining with uncertain data. In: SIGKDD'09, pp. 29–38.
  10. Agrawal, R. , Srikant, R. , 1994. Fast algorithms for mining association rules. In: VLDB'94, pp. 487–499.
  11. Bernecker, T. , Kriegel, H. , Renz, M. , Verhein, F. , Zue?e, A. , 2009. Probabilistic frequent itemset mining in uncertain databases. In: SIGKDD'09, pp. 119–127.
  12. Grahne, G. , Zhu, J. , 2005. Fast algorithms for frequent itemset mining using fp-trees. IEEE Transactions on Knowledge and Data Engineering 17 (10), 1347–1362.
  13. Gupta, R. , Fang, G. , Field, B. , Steinbach, M. , Kumar, V. , 2008. Quantitative evaluation of approximate frequent pattern mining algorithms. In: SIGKDD'08, pp. 301–309.
  14. Han, J. , Pei, J. , Yin, Y. , 2000. Mining frequent patterns without candidate generation. In: SIGMOD'00, pp. 1–12.
  15. Le, B. , Nguyen, H. , Vo, B. , 2011. An ef?cient strategy for mining high utility itemsets. International Journal of Intelligent Information and Database Systems 5 (2), 164–176.
  16. Lin, K. C, Liao, I. E. , Chen, Z. S. , 2011. An improved frequent pattern growth method for mining association rules. Expert Systems with Applications 38 (5), 5154–5161.
  17. Liu, B. , Hsu, W. , Ma, Y. , 1998. Integrating classi?cation and association rule mining. In: SIGKDD'98, pp. 80–86.
  18. Lucchese, B. , Orlando, S. , Perego, R. , 2006. Fast and memory ef?cient mining of frequent closed itemsets. IEEE Transactions on Knowledge and Data Engineering 18 (1), 21–36.
  19. Vo, B. , Le, B. , 2011. Interestingness measures for mining association rules: combination between lattice and hash tables. Expert Systems with Applications 38 (9), 11630–11640.
  20. Vo, B. , Coenen, F. , Le, B. , 2013. A newmethod for mining frequent weighted itemsets based on WIT-trees. Expert Systems with Applications 40 (4), 1256–1264.
  21. Wang, J. , Han, J. , Pei, J. , 2003. CLOSET+: searching for the best strategies for mining frequent closed itemsets. In: SIGKDD'03, pp. 236–245.
  22. Yun, U. , Shin, H. , Ryu, K. H. , Yoon, E. , 2012. An ef?cient mining algorithm for maximal weighted frequent patterns in transactional databases. Knowledge Based Systems 33, 53–64.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Erasable itemset mining pidset dpidset