Advance Mining of Temporal High Utility Itemset

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Swati Soni 1,* Sini shibu 2

1. Department of computer science, Technocrats Institute of Technology Bhopal, India

2. H.O.D Department of computer science, Technocrats Institute of Technology Bhopal, India

* Corresponding author.


Received: 14 Jun. 2011 / Revised: 7 Oct. 2011 / Accepted: 10 Dec. 2011 / Published: 8 Apr. 2012

Index Terms

utility mining, temporal high utility itemsets, data streams, association rules, stocks


The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the financial markets and help in stock-price forecasting. Therefore, we propose in this paper a portfolio management solution with business intelligence characteristics. We know that the temporal high utility itemsets are the itemsets with support larger than a pre-specified threshold in current time window of data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. We proposed the novel algorithm for temporal association mining with utility approach. This make us to find the temporal high utility itemset which can generate less candidate itemsets.

Cite This Paper

Swati Soni, Prof. Sini shibu, "Advance Mining of Temporal High Utility Itemset", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.4, pp.26-32, 2012. DOI:10.5815/ijitcs.2012.04.04


[1]Agrawal, R., Imielinski, T., and Swami, A. Mining association rules between sets of items in large databases. In Proceedings of 1993 ACM SIGMOD Intl. Conf. On Management of Data, pages 207--216, Washington, D. C.,May 1993.

[2]Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. I. Fast discovery of association rules. In book Advances in Knowledge Discovery and Data Mining, pages 307--328. AAAI/MIT Press, 1996.

[3]Agrawal, R., and Srikant, R. Mining Sequential Patterns. Proceedings of the 11th International Conference on Data Engineering, pages 3-14, March 1995.

[4]Ayn, N.F., Tansel, A.U., and Arun, E. An efficient algorithm to update large itemsets with early pruning. Technical Report BU-CEIS-9908 Dept of CEIS Bilkent Uniiversity, June 1999.

[5]Ayn, N.F., Tansel, A.U., and Arun, E. An efficient algorithm to update large itemsets with early pruning. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, August 1999.

[6]Bettini, C., Wang, X. S., and Jajodia, S. Testing complex temporal relationships involving multiple granularities and its application to data mining. In Proc.of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 3-5, 1996, Montreal, Canada, pages 68–78. ACM Press, 1996.

[7]Chan, R., Yang, Q., and Shen, Y. Mining high utility Itemsets. Proc. of IEEE ICDM, Florida, 2003.

[8]Cheung, D., Han, J., Ng, V., and Wong, C.Y. Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. Proc. of 1996 Int’l Conf. on Data Engineering, pages 106—114, February 1996.

[9]Cheung, D., Lee, S.D., and Kao. B., A General Incremental Technique for Updating Discovered Association Rules. Proc. International Conference On Database Systems For Advanced Applications, April 2008.

[10]Chi, Y., Wang, H., Yu, P. S., and Richard, R. Muntz: Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window Proceedings of the 2004 IEEE International Conference on Data Mining (ICDM'04).

[11]Das, G., Lin, K. I., Mannila, H., Renganathan G., and Smyth, P. Rule Discovery from Time Series. Proceedings of the 4th ACM SIGKDD, pages 16—22, August 1998.

[12]Lee, C. H., Lin, C. R., and Chen, M. S. Sliding-window filtering: An efficient algorithm for incremental mining. In Intl. Conf. on Information and Knowledge Management (CIKM01), pages 263??270, November 2009.

[13]Cláudia M. Antunes , and Arlindo L. Oliveira. Temporal Data Mining: an overview: Instituto Superior Técnico, Dep. Engenharia Informática, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal, page 2-15.

[14]Dunham, Margaret H: Datamining: Introductory and advanced topics, ch 6. Prentice Hall; 1 edition (September 1, 2002). ISBN: 0130888923

[15]Weigend, A., Chen, F., Figlewski, S., Waterhouse, S.R.: Discovering Technical Trades in the T-Bond Futures Market. Proc. 4th Int. Conf. Knowledge Discovery and Data Mining (KDD ’98), New York, NY USA (1998) 354-358.