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General Information

Prof. Pascal Lorenz
University of Haute Alsace, France
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.

IJFCC 2013 Vol.3(1): 55-59 ISSN: 2010-3751
DOI: 10.7763/IJFCC.2014.V3.267

Mining Temporal Association Rules in Network Traffic Data

Guojun Mao
 Abstract—Mining association rules is one of the most important and popular task in data mining. Current researches focus on discovering frequent itemsets that is an important step to it. Many algorithms for discovering frequent itemsets have been proposed. However, for a large database, an efficient mining algorithm must be a better balance in I/O cost and main memory load. Most traditional algorithms, like Aprioir [Agrawal, 1993], often take higher I/O cost because of multi-scan over the analyzed database. There have been a few of algorithms, like FP-Tree [Han, 2000], use a limited pass numbers to databases, but they could suffer from the shortage of main memory as there does not consider time constraints to association rules. In the paper, we first discuss the problem of mining temporal association rules in databases. Then, we create the necessary sub-operators between itemsets and interval operators between time intervals to mine temporal association rules. Finally, a new algorithm called MTAR_Sub for mining temporal association rules is designed and discussed.

Index Terms—Association rule, data mining, frequent itemset, network traffic, temporal constraint.

Guojun Mao is with the School of Information, Central University of Finance & Economics, Beijing, China, 100081 (e-mail: maximmao@hotmail.com).


Cite:Guojun Mao, "Mining Temporal Association Rules in Network Traffic Data," International Journal of Future Computer and Communication vol. 2, no. 6, pp. 55-59, 2014.

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