Survey on Utility Data mining

IJCSEC Front Page

Abstract:
Data Mining is an activity that extracts some new useful information contained in large databases. Traditional data mining methodologies focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Association Rule Mining focuses on existence of an item in a transaction, whether or not it is purchased. The drawbacks of frequent itemset mining leads to consider a utility mining, which allows a user to conveniently express the usefulness of itemsets as utility values and then find itemsets with high utility values. In practice the utility value of an itemset can be profit, popularity, or some other measures of user’s preference. There exist several algorithms in literature to mine high utility itemsets. In this paper, a literature survey of various high utility itemset mining algorithms has been presented.

Keywords: Data Mining; Frequent pattern Mining; Utility Mining.

References:

  1. R. Agrawal , T. Imielinski, A. Swami, 1993, mining association rules between sets of items in large databases, in: proceedings of the ACM SIGMOD International Conference on Management of data, pp. 207-216
  2. J Han, J Pei, and Y Yin, “Mining Frequent Patterns without Candidate Generation,” Proc ACM-SIGMOD Int’l Conf Management of Data, pp. 1-12, 2000.
  3. H.Yao, H.J.Hamilton, C.J.Butz, A foundation approach to mining itemset utilities from databases,in:Proceedings of the Third SIAM International Conference on Data Mining, Orlando, Florida , 2004,pp.482-486
  4. H.Yao,H.J.Hamilton ,Mining itemset utilities from transacation databases, in Data and Knowledge Enineering 59(2006) pp.603-626
  5. Liu. Y, Liao. W,A. Choudhary, A fast high utility itemsets mining algorithm, in: Proceedings of the Utility-Based Data Mining Workshp, August 2005
  6. V.S.Tseng ,C.J. Chu , T.Liang, Efficient mining of temporal high utility itemsets from data streams, in: Proceedings of Second International Workshop on Utility-Based Data Mining , August 20, 2006
  7. H.F.Li, H.Y. Huang , Y.Cheng Chen, y. Liu, S.Lee, Fast and memory efficient mining of high utility itemsets in data streams, in :Eigth International Conference of Data Mining 2008
  8. J.Hu, A. Mojsilovic , High-utility pattern mining :A method for discovery of high-utility ietmsets,in :Pattern Recognition 40(2007) 3317-3324
  9. 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
  10. Cheng Wei Wu1, Bai-En Shie1, Philip S. Yu2, Vincent S. Tseng1 Mining Top-K High Utility Itemsets KDD’12, August 12–16, 2012, Beijing, China. Copyright 2012 ACM 978-1-4503-1462-6/12/08
  11. A.Erwin, R.P.Gopalan,N.R.Achuthan, Efficient mining of high utility itemsets from large datasets, in: Advances in Knowledge Discovery , Springer Lecture Notes in Computer Science , volume 5012/2008, pp. 554-561
  12. J.Pillai, O.P.Vyas, S. Soni, M.Muyeba, A conceptual approach to temporal weighted itemset utility mining, in : International Journal of Computer Applications (0975-8887) Volume 1-No.28, 2010
  13. Vincent S. Tseng, Cheng-Wei Wu, Philippe Fournier-Viger, and Philip S. Yu, “Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets”, IEEE Transactins on Knowledge and Data Engineering, Vol. 27, No. 3, 2015.
  14. uangzhou Yu, Shihuang Shao and Xianhui Zeng mining long high utility itemsets in transaction databases wseas transactions on information science & applications issue 2, volume 5, feb. 2008.