Fast Algorithms for Temporal Association Rules in a Large Database

Abstract:

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As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

Info:

Periodical:

Key Engineering Materials (Volumes 277-279)

Edited by:

Kwang Hwa Chung, Yong Hyeon Shin, Sue-Nie Park, Hyun Sook Cho, Soon-Ae Yoo, Byung Joo Min, Hyo-Suk Lim and Kyung Hwa Yoo

Pages:

287-292

DOI:

10.4028/www.scientific.net/KEM.277-279.287

Citation:

L. N. Byon and J. H. Han, "Fast Algorithms for Temporal Association Rules in a Large Database", Key Engineering Materials, Vols. 277-279, pp. 287-292, 2005

Online since:

January 2005

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Price:

$35.00

[1] D. Peppers and M. Rogers : Always customer made by 1: 1 Marketing revolution : CM business(1995).

[2] R. Agrawal, T. Imielinski and A. Swami: Mining Association Rules between Sets of Items in Large Database, Proceedings of ACM SIGMOD Conference on Management of Data, Washington D.C. (1993), pp.207-216.

DOI: 10.1145/170035.170072

[3] R. Agrawal and R. Strikant: Fast Algorithms for Mining Association Rules, Proceedings of the 20 th International Conference on Very Large Databases, Satiago, Chile, (1994).

[4] B. Ozden, S. Ramaswamy and A. Silberschatz: Cyclic Association rules, proceeding of International Conference on Data Engineering. (1998), pp.412-421.

DOI: 10.1109/icde.1998.655804

[5] Chang-Hung Lee, Jian Chih Ou, and Ming-Syan Chen: Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining, Proceedings of the Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD, Seoul , Korea (2003).

DOI: 10.1007/3-540-36175-8_45

[6] J. S. Hunter: The Exponentially Weighted Moving Average, Journal of Quality Technology, Vol. 18, pp.203-210, (1986).

[7] X. Chen and I. Petrounias: A framework for temporal data mining, In Proc. Ninth International Conference on Database and Expert Systems Applications, DEXA, (1998).

[8] Y. Li and P. Ning: Discovering Calendar-based Temporal Association Rules, Proceedings of the 8th International Symposium on Temporal and Reasoning, (2001).

DOI: 10.1109/time.2001.930706

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