Enhancing Ability of User Personalization by Application of Rough Fuzzy Grouping Mechanism for Improved Web Intelligence
Manas Kumar Yogi1, L. Yamuna2
1Manas Kumar Yogi, Assistant Professor, Department of Computer Science Engineering, Pragati Engineering College Autonomous, Surampalem, East Godavari (AP), India.
2M.Yamuna, Assistant Professor, Department of Computer Science Engineering, Pragati Engineering College Autonomous, Surampalem, East Godavari (AP), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 718-722 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2854028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In contemporary world, Web personalization tenders accurate means for the evolution of operations that have the enticing feature to satisfy compelling obligation of their end user. To perform that, developers of web need to face an decisive trial regarding the disclosure of information of concern which the end users show while they reach out to various sites. Web Usage Mining is a functioning exploration region which regards the disclosure of helpful examples of run of the mill client practices by using utilization information. Grouping has been hugely applied for sake of classifying users having identical concerns. Rough fuzzy grouping proves to be an mechanism handy to deduce user sections from web use information accessible via server history files. It is well known that fuzzy grouping works on mechanism of distance-based metrics to judge the similarity among user choices. But the application of such techniques may propel to feeble outcomes by classifying user groups that do not include the meaningful knowledge assimilated . In this paper, we advocate an technique based on a rough fuzzy grouping algorithm armed with a rough fuzzy similarity metric to deduce user groups. For pertinence, we deploy the presented technique on users data extricated from server history files of a popular web site
Keyword: Rough, Fuzzy, Similarity Measures, Grouping, Personalization, user Categorization.
Scope of the Article: Fuzzy Logics