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Fuzzy Association Rule Mining- A Survey
Pramod Pardeshi1 , Ujwala Patil2
  • Dept. of Computer Engineering, R. C. Patel Institute of Technology Shirpur, MS, India.
  • Dept. of Computer Engineering, R. C. Patel Institute of Technology Shirpur, MS, India.
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Section:Survey Paper, Product Type: Isroset-Journal
Vol.5 , Issue.6 , pp.13-18, Dec-2017


Online published on Dec 31, 2017
Copyright © Pramod Pardeshi, Ujwala Patil . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Citation :
IEEE Style Citation: Pramod Pardeshi, Ujwala Patil, “Fuzzy Association Rule Mining- A Survey”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.13-18, 2017.

MLA Style Citation: Pramod Pardeshi, Ujwala Patil "Fuzzy Association Rule Mining- A Survey." International Journal of Scientific Research in Computer Science and Engineering 5.6 (2017): 13-18.

APA Style Citation: Pramod Pardeshi, Ujwala Patil, (2017). Fuzzy Association Rule Mining- A Survey. International Journal of Scientific Research in Computer Science and Engineering, 5(6), 13-18.
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Abstract :
The World Wide Web has become a huge repository of the hypertexts and documents. The rapid growth of web logs and texts has gained a lot of attention from the researchers for extracting the interesting rule for designing of the web pages, drawing the customer preference, analysing the customer behaviour and decision making for serving the organizations with better services. Such decisions are made by analysing different web parameters such as the server log, registration information, access time, session period, page hits and other relative information left by user. This paper presents a survey on various techniques such as fuzzy logic and rule mining for finding the customer behaviour that helps in better decision making and enhancing the performance of the system.
Key-Words / Index Term :
Video watermarking, feature selection, rough set theory, motion vectors, particle swarm optimization
References :
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