High Utility ITEMSET Mining from Large Database

Authors(2) :-Annusooya. A, T. Seeniselvi

Frequent itemset mining is one of the main problems in data mining. It has practical importance in a wide range of application areas such as decision support, Web usage mining, bioinformatics, etc. A number of relevant algorithms have been proposed in recent years for the fast access of data from the database. Mining high utility itemsets from a large database refers to the discovery of itemsets with high utility like profits. The proposed work is to mine the high utility items from the large database. The traditional association rule mining algorithm is used to find out the frequently occurring patterns of item sets. Apriori algorithm is used to find the high utility itemset. Data about the products are collected and stored in a database. Whenever customers buy the same product repeatedly the frequent pattern is formed and the infrequent items are separated. The high utility itemset is based on the user-specified utility threshold or it is a low-utility itemset. Admin maintain the entire system process like workers details, user details, product sales, raw materials. Admin can generate report based on the product sales. Admin can generate the Apriori products based on the threshold value. Admin can generate the graph for the frequently purchased products.

Authors and Affiliations

Annusooya. A
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India
T. Seeniselvi
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India

Frequent Data Mining (FDM), Association Rule Mining, Apriori Algorithm.

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Database. In: ACM SIGMOD International Conference on Management of Data (1993).
  2.  A. Erwin, R.P. Gopalan, and N.R. Achuthan,“Efficient Mining of High Utility Itemsets from Large Datasets”, T. Washio et al. (Eds.): PAKDD2008, LNAI 5012, pp. 554–561, 2008. © Springer-Verlag Berlin Heidelberg 2008.
  3.  Chan , Q.Yang,Y.D Shen, Mining high utility itemsets, in: Proceedings of the 3rd IEEE International Conference on Data Mining , Melbourne , Florida, 2003, pp.19-26.
  4.  J Han, J.Pei, Y.Yin ,R. Mao Mining frequent Patterns without candidate generation:a frequent -pattern tree approach , Data Mining and Knowledge Discovery 8(1)(2004) 53-87
  5. J.Hu, A. Mojsilovic , High-utility pattern mining :A method for discovery of high-utility ietmsets,in :Pattern Recognition 40(2007) 3317-3324
  6. Erwin, A., Gopalan, R.P., Achuthan, N.R, “A Bottom-Up Projection Based Algorithm for Mining High Utility Itemsets”, In: International Workshop on Integrating AI and Data Mining. Gold Coast, Australia (2007).
  7. Yao, H., Hamilton, H.J., Buzz, C. J., “A Foundational Approach to Mining Itemset Utilities from Databases”, In: 4th SIAM International Conference on Data Mining, Florida USA (2004).
  8.  R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. 20th Int. Conf. Very Large Data Bases, 1994, pp. 487–499.
  9. Yao, H., Hamilton, H.J., “Mining itemset utilities from transaction databases”, Data & Knowledge Engineering 59(3), 603–626 (2006).
  10. Gouda and M. J. Zaki, “Efficiently mining maximal frequent itemsets,” in Proc. IEEE Int. Conf. Data Mining, 2001, pp. 163–170.
  11. S.Shankar Dr. T .purusothaman, Kannimuthu s a novel utility and frequency based itemset mining approach for improving crm in retail business 2010 international journal of computer applications (0975 - 8887) volume 1 – no. 16
  12. C.-W. Lin, T.-P. Hong, and W.-H. Lu, “An effective tree structure for mining high utility itemsets,” Expert Syst. Appl., vol. 38, no. 6, pp. 7419–7424, 2011.
  13. G.-C. Lan, T.-P. Hong, and V. S. Tseng, “An efficient projectionbased indexing approach for mining high utility itemsets,” Knowl. Inf. Syst, vol. 38, no. 1, pp. 85–107, 2014.
  14. Han, J., Wang, J., Yin, Y., “Mining frequent patterns without candidate generation”, In: ACM SIGMOD International Conference on Management of Data (2000).
  15.  Smita R. Londhe , Rupali A. Mahajan , Bhagyashree J. Bhoyar “Overview on Methods for Mining High Utility Itemset from Transactional Database” International Journal of Scientific Engineering and Research (IJSER), Volume 1 Issue 4, December 2013

Publication Details

Published in : Volume 1 | Issue 6 | November-December 2015
Date of Publication : 2015-12-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 198-202
Manuscript Number : IJSRSET151594
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Annusooya. A, T. Seeniselvi, " High Utility ITEMSET Mining from Large Database, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 6, pp.198-202, November-December-2015.
Journal URL : http://ijsrset.com/IJSRSET151594

Article Preview