An Efficient Web Personalization Approach to Discover User Interested Directories
| Autor: | A. Adhiselvam, Robinson Joel M, M. V. Srinath |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Apriori algorithm
Fuzzy clustering Information retrieval lcsh:Computer engineering. Computer hardware Multimedia Computer Networks and Communications Computer science Advanced Apriori Algorithm Probabilistic logic lcsh:TK7885-7895 Directory computer.software_genre Personalization World Wide Web ComputingMethodologies_PATTERNRECOGNITION Fuzzy Clustering Algorithm Web mining Web Directories and Web Personalization Pattern Analysis Cluster analysis computer |
| Zdroj: | ICTACT Journal on Soft Computing, Vol 4, Iss 3, Pp 760-766 (2014) |
| ISSN: | 1798-0461 |
| DOI: | 10.4304/jetwi.6.1.142-148 |
| Popis: | Web Usage Mining is the application of data mining technique used to retrieve the web usage from web proxy log file. Web Usage Mining consists of three major stages: preprocessing, clustering and pattern analysis. This paper explains each of these stages in detail. In this proposed approach, the web directories are discovered based on the user’s interestingness. The web proxy log file undergoes a preprocessing phase to improve the quality of data. Fuzzy Clustering Algorithm is used to cluster the user and session into disjoint clusters. In this paper, an effective approach is presented for Web personalization based on an Advanced Apriori algorithm. It is used to select the user interested web directories. The proposed method is compared with the existing web personalization methods like Objective Probabilistic Directory Miner (OPDM), Objective Community Directory Miner (OCDM) and Objective Clustering and Probabilistic Directory Miner (OCPDM). The result shows that the proposed approach provides better results than the aforementioned existing approaches. At last, an application is developed with the user interested directories and web usage details. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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