Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : V Sangavi | P Divya

Volume 04 Issue 03 2017

ISSN no:  2348-3121

Page no: 236-238

Abstract –Frequent item set mining is another advancement of information mining innovation. Among utility mining issues, utility mining with the item set offer system is a hard one as no against monotonicity property holds with the interestingness measure. High utility mining is not scalable and efficient with large database. In existing system it has two stage approach, in first phase it finds candidates of high utility pattern and in other phase it scan the raw data once again to identify the high utility pattern from the candidate. In proposed system it has one phase which scans the raw data directly to identify the high utility mining. Apriori algorithm is used to find pair product purchase and High utility item set with negative item value algorithm is efficient for large database.

Keywords – Frequent Item Set, Apriori Algorithm, High Utility Mining


  1. Efficient tree structures for high utility pattern mining in incremental data- bases, IEEE Trans. Knowl. Data Eng., C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee, vol. 21, no. 12, pp. 1708– 1721, Dec. 2009.
  2. Mining high utility itemsets without candidate generation, in Proc. ACM Conf. Inf.Knowl. Manage., M.Liu and J.Qu, 2012,pp.55–64.
  3. An efficient projection- based indexing approach for mining high utility itemsets, Knowl. Inf. Syst., G.-C. Lan, T.-P. Hong, and V. S. Tseng, vol. 38, no. 1, pp. 85–107, 2014.
  4. High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates, Expert Syst. Appl., U. Yun, H. Ryang, and K. H. Ryu, vol. 41, no. 8, pp. 3861–3878, 2014.
  5. Efficient algorithms for mining high utility itemsets from transactional data- bases, IEEE Trans. Knowl. Data Eng., V. S. Tseng, B.-E. Shie, C.-W. Wu, and P. S. Yu, vol. 25, no. 8, pp. 1772–1786, Aug. 2013.
  6. A unified framework for utility-based measures for mining itemsets, in Proc. ACM SIGKDD 2nd Workshop Utility-Based Data Mining, 2006, H. Yao, H. J. Hamilton, and L. Geng, pp. 28–37.
  7. FHM: Faster high-utility item set mining using estimated utility co- occurrence pruning, in Proc. 21st Int. Symp. Found. Intell. Syst., P. Fournier-Viger, C.-W. Wu, S. Zida, and V. S. Tseng, 2014, pp. 83–92.
  8. UP-Hist tree: An efficient data structure for mining high utility patterns from transaction databases, in Proc. 19th Int. Database Eng. Appl. Symp., S. Dawar and V. Goyal, 2015, pp. 56–61.
  9. Direct discovery of high utility itemsets without candidate generation, in Proc. IEEE 12th Int. Conf. Data Mining, J. Liu, K. Wang, and B. Fung, 2012, pp. 984–989.
  10. Efficient mining of high utility itemsets from large data sets, in Proc. 12th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, A. Erwin, R. P. Gopalan, and N. R. Achuthan, 2008, pp. 554–561.
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