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A Novel Approach to Mine Temporal Association Rules

T. Mathu, S. Geetha

Abstract


Given a large temporal transaction database, the aim of this paper is to discover the itemsets that are having related support during a particular event over time and to find the association between those items. Most works in mining association rules involve the generation of frequent items which is the core process in generating association rules. It is necessary to scan database in each timeslot to generate frequent items in temporal data mining. This incurs much cost when the number of transactions is large. In this paper, we propose an approach that utilizes the concept of tight lower and upper bounds of supports at different time intervals. It reduces the number of candidates to be scanned in database. Also our method helps in finding association between items that gets related support when a particular event occurs. Our experimental results proves that the association rules generated from the candidate items are more accurate and incurs less cost than the traditional rule mining methods.

Keywords


Lower and Upper Bound Supports, Related Items, Temporal Association Rule, Temporal Data Mining.

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References


B. Ozden, S. Ramaswamy, and A. Silberschatz, “Cyclic Association Rules,” Proc. IEEE Int’l Conf. Data Eng. (ICDE), 1998.

Y. Li, P. Ning, X.S. Wang, and S. Jajodia, “Discovering Calendar- Based Temporal Association Rules,” J. Data and Knowledge Eng., vol. 15, no. 2, 2003.

Jin Soung Yoo and Shashi Shekhar, “Similarity-Profiled Temporal Association Mining,” IEEE Transactions on Knowledge And Data Eng., vol. 21, no. 8, Aug 2009.

R. Agarwal and R. Srikant, “Fast Algorithms for Mining Association Rules”, Proc. Int’l Conf. Very Large Databases (VLDB), 1994.

J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proc. ACM SIGMOD, 2000.

J. Han and Y. Fu, “Discovery of Multi-Level Association Rules from Large Databases,” Proc. Int’l Conf. Very Large Databases (VLDB), 1995.

J. Park, M. Chen, and P. Yu, “An Effective Hashing-Based Algorithm for Mining Association Rules,” Proc. ACM SIGMOD, 1995.

R. Srikant and R. Agrawal, “MINing Generalized Association Rules,” Proc. Int’l Conf. Very Large Databases (VLDB), 1995.

S. Ramaswamy, S. Mahajan, and A. Silberschatz, “On the Discovery of Interesting Patterns in Association Rules,” Proc.Int’l Conf. Very Large Databases (VLDB), 1998.

Y. Li, S. Zhu, X.S. Wang, and S. Jajodia, “Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets,” J. Information Sciences, vol. 176, no. 8, 2006.

G. Dong and J. Li, “Efficient Mining of Emerging Patterns: Discovering Trends and Differences,” Proc. ACM SIGKDD, 1999.

T. Calders, “Deducing Bounds on the Frequency of Itemsets,” Proc. EDBT Workshop Database Techniques in Data Mining (DTDM), 2002.

C. Hidber, “Online Association Rule Mining,” Proc. ACM SIGMOD, 1998.

R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc.IEEE Int’l Conf. Data Eng. (ICDE), 1995.

W. Teng, M. Chen, and P. Yu, “A Regression-Based Temporal Pattern Mining Scheme for Data Streams,” Proc. Int’l Conf. Very Large Databases (VLDB), 2003.

D. Gunopulos and G. Das, “Time Series Similarity Measures,” Tutorial Notes of the ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, 2000.

B. Yi and C. Faloutsos, “Fast Time Sequence Indexing for Arbitrary Lp Norms,” Proc. Int’l Conf. Very Large Databases (VLDB), 2000.

Qian Wan and Aijun An, “Discovering Transitional Patterns and Their Significant Milestones in Transaction Databases,” IEEE Transactions on Knowledge And Data Eng., vol. 21, no. 12, December 2009.

Asem Omari, Regina Langer and Stefan Conrad, “TARtool: A Temporal Dataset Generator for Market Basket Analysis” Lecture Notes in Computer Science, 2008, Volume 5139/2008,400-410, DOI: 10.1007/978-3-540-88192-6_37


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