NDLI: Web Usage Classification and Clustering Approach for Web Search Personalization

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Web Usage Classification and Clustering Approach for Web Search Personalization

Content Provider	ACM Digital Library
Author	Jena, Sudarson Vijayalakshmi, K.
Abstract	The increases in the information resources on the World Wide Web in search of the necessary information, as users navigate the Web with multiple sites. When user surfing the web which is a huge and complicated often miss their required searching pages. Web personalization is based on the Web usage logs of user's makes advantage of the knowledge required for the analysis of the content and structure of web sites promising to solve this problem by supporting one of the procedures. The search engine can affect the effectiveness of existing approaches, depending on the user profile, which is building more and more on the web pages or documents. In this paper, we propose an efficient and novel web search based on the individual classification and clustering method. The proposed approach classified the cluster data using frequent pattern mining and multilevel association rules for recurring relationship and cluster the web usage using Hierarchical methods with the navigating site and user interest for personalization. This approach process in advance to support the real time personalization and minimizes the cost reduction of preparation personalization resource in real time. The proposed approach is an effective personalization to the user's interest; in experimental research it has shown high precision measures.
Starting Page	376
Ending Page	383
Page Count	8
File Format	PDF
ISBN	9781450335522
DOI	10.1145/2818567.2818677
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Classification Web logs Web usage Clustering Data mining Web search
Content Type	Text
Resource Type	Article

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