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An Efficient Algorithm for Mining Frequent Itemsets within Large Windows over Data Streams
Mahmood Deypir, Mohammad Hadi Sadreddini
Pages - 119 - 125     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 2   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
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KEYWORDS
Data Stream Mining , Frequent Itemsets, Sliding Window
ABSTRACT
Sliding window is an interesting model for frequent pattern mining over data stream due to handling concept change by considering recent data. In this study, a novel approximate algorithm for frequent itemset mining is proposed which operates in both transactional and time sensitive sliding window model. This algorithm divides the current window into a set of partitions and estimates the support of newly appeared itemsets within the previous partitions of the window. By monitoring essential set of itemsets within incoming data, this algorithm does not waste processing power for itemsets which are not frequent in the current window. Experimental evaluations using both synthetic and real datasets shows the superiority of the proposed algorithm with respect to previously proposed algorithms.
CITED BY (8)  
1 Hung, L. N., Thu, T. N. T., & Nguyen, G. C. (2015). An Efficient Algorithm in Mining Frequent Itemsets with Weights over Data Stream Using Tree Data Structure. International Journal of Intelligent Systems and Applications (IJISA), 7(12), 23.
2 Mala, A., & Ramesh, D. F. (2014, August). Web Log Mining to Enhance Surfing Experience. In Applied Mechanics and Materials (Vol. 626, pp. 7-13). Trans Tech Publications.
3 Mathai, P. P., & Balan, R. S. An Extensive Review of Significant Researches in Data Mining.
4 Nguyen, T. T., & Nguyen, P. K. (2013). A New Viewpoint for Mining Frequent Patterns. Editorial Preface, 4(3).
5 Li Haifeng, Zhang Ning, Zhu Jianming, & Caohuai Hu. (2012) itemsets time-sensitive data stream mining algorithms. Journal of Computers, 35 (11), 2283-2293.
6 Nguyen, T. T., & Nguyen, P. K. (2012). A new approach for problem of sequential pattern mining. In Computational Collective Intelligence. Technologies and Applications (pp. 51-60). Springer Berlin Heidelberg.
7 Chandrika, J., & Kumar, K. A. (2012). Frequent Itemset Mining in Transactional Data Streams Based on Quality Control and Resource Adaptation. International Journal of Data Mining & Knowledge Management Process, 2(6), 1.
8 Bangalore, M. H. S. (2012). Resource adaptive technique for frequent itemset mining in transactional data streams. IJCSNS, 12(10), 87.
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Mr. Mahmood Deypir
- Iran
mdeypir@gmail.com
Associate Professor Mohammad Hadi Sadreddini
- Iran


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