International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)

IJTEEE >> Volume 2 - Issue 7, July 2014 Edition

International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289

Concept Change Aware Dynamic Sliding Window Based Frequent Itemsets Mining Over Data Streams

[Full Text]



K. Neeraja, V. Sireesha



Keywords: Data Mining, Data streaming, Frequent Itemsets, Concept Change.



ABSTRACT:Considering the continuity of a data stream, the accessed windows information of a data stream may not be useful as a concept change is effected on further data. In order to support frequent item mining over data stream, the interesting recent concept change of a data stream needs to be identified flexibly. Based on this, an algorithm can be able to identify the range of the further window. A method for finding frequent itemsets over a data stream based on a sliding window has been proposed here, which finds the interesting further range of frequent itemsets by the concept changes observed in recent windows.



[1]. Mahmood Deypir a, Mohammad Hadi Sadreddini, Sattar Hashemi, Towards a variable size sliding window model for frequent itemset mining over data streams.

[2]. Chandni Shah1 , Factors Influencing Frequent Pattern Mining on Stream Data.

[3]. Pramod S, O.P. Vyas, Survey on Frequent Item set Mining Algorithms.

[4]. Zijian Zheng, Ron Kohavi, Real World Performance of Association Rule Algorithms.

[5]. Zhen-Hui Song, Yi Li, Associative classification over Data Streams.

[6]. Mohamed Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy, Mining Data Streams: A Review.

[7]. Nan Jiang and Le Gruenwald, Research Issues in Data Stream Association Rule Mining.

[8]. Christian Borgelt, Efficient Implementations of Apriori and Eclat.

[9]. Lars Schmidt-Thieme, Computer-based New Media Group (CGNM), Algorithmic Features of Eclat.

[10]. Manku, G. S., & Motwani, R. (2002). Approximate frequency counts over data streams. In Proc. VLDB int. conf. very large databases (pp. 346357).

[11]. Chang, J. H., & Lee, W. S. (2005). estWin: Online data stream mining of recent frequent itemsets by sliding window method. Journal of Information Science, 31(2), 7690.

[12]. Mozafari, B., Thakkar, H., & Zaniolo, C. (2008). Verifying and mining frequent patterns from large windows over data streams. In Proc. int. conf. ICDE (pp. 179 188).

[13]. Aggarwal, C. (2003). A framework for diagnosing changes in evolving data streams.

[14]. Koh, J.- L., & Lin, C.- Y. (2009). Concept shift detection for frequent itemsets from sliding window over data streams.

[15]. http://people.revoledu.com/kardi/tutorial/Similarity/Jaccard.html

[16]. Data stream mining - Wikipedia, the free encyclopedia

[17]. Jiawei Han (http://www.sal.cs.uiuc.edu/~hanj/DM_Book.html)

[18]. Vipin Kumar (http://www.users.cs.umn.edu/~kumar/csci5980/index.html)