INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.7, No.11, Oct. 2015

An Efficient Algorithm for Mining Weighted Frequent Itemsets Using Adaptive Weights

Full Text (PDF, 458KB), PP.41-48


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Author(s)

Hung Long Nguyen

Index Terms

Data mining;Knowledge discovery;Weighted frequent itemset mining;Adaptive weight;Pattern growth techninque

Abstract

Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary with time. Therefore, reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. Recently, Chowdhury F. A. et al. have introduced a novel concept of adaptive weight for each item and propose an algorithm AWFPM (Adaptive Weighted Frequent Pattern Mining). AWFPM can handle the situation where the weight (price or significance) of an item may vary with time. In this paper, we present an improved algorithm named AWFIMiner. Experimental computations show that our AWFIMiner is more efficient and scalable for mining weighted frequent itemsets using adaptive weights. Moreover, because it only requires one single database scan, the AWFIMiner is applicable for mining these itemsets on data streams.

Cite This Paper

Hung Long Nguyen,"An Efficient Algorithm for Mining Weighted Frequent Itemsets Using Adaptive Weights", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.11, pp.41-48, 2015. DOI: 10.5815/ijisa.2015.11.06

Reference

[1]Agrawal, R., Srikant, R., Fast Algorithms for Mining Association Rules. In: 20th Int. Conf. on Very Large Data Bases (VLDB), 1994, 487–499.

[2]Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W., Mining association rules with weighted items. In Proceedings of Intl. Database Engineering and Applications Symposium (IDEAS 1998), Cardiff, Wales, UK, July 1998, 68–77.

[3]Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong, Young-Koo Lee, Mining Weighted Frequent Patterns Using Adaptive Weights. In: Fyfe et al. (Eds.): IDEAL 2008, LNCS 5326, 2008, 258–265.

[4]Darshan M. Tank, Improved Apriori Algorithm for Mining Association Rules, Int. Jour. Information Technology and Computer Science, 2014, 07, 15-23.

[5]Han, J., Pei, J., Yin, Y., Mao, R., Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 2004, 53–87.

[6]Han, J., H., Xin, D., and Yan, X., Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, vol. 15, 2007, 55–86.

[7]Han J., and Kamber M., Data Mining: Concepts and Techniques, Morgan Kanufmann, 2000.

[8]Jiang, N., Gruenwald, L., Research Issues in Data Stream Association Rule Mining. SIGMOD Record 35(1), 2006, 14–19.

[9]Paray S.M. Tsai, Mining frequent itemsets in data streams using the weighted sliding window model. Expert Systems with Applications 36, 2009, 11617-11625.

[10]Tao, F., Weighted association rule mining using weighted support and significant framework. In: 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, USA, 2003, 661–666. 

[11]Wang, W., Yang, J., Yu, P.S., WAR: weighted association rules for item intensities. Knowledge Information and Systems 6, 2004, 203–229.

[12]Yun, U., Leggett, J.J., WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Fourth SIAM Int. Conf. on Data Mining, USA, 2005, 636–640.

[13]Yun, U., Efficient Mining of weighted interesting patterns with a strong weight and/or support affinity. Information Sciences 177, 2007, 3477–3499.

[14]Yun, U., An efficient mining of weighted frequent patterns with length decreasing support constraints. Knowledge-Based Systems, Volume 21 Issue 8, December 2008, 741-752.

[15]Zhang, S., Zhang, C.,Yan, X., Post-mining: maintenance of association rules by weighting. Information Systems 28, 2003, 691–707.