Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques | Zendy

G Shivaprasad | Zendy; N. V. Subba Reddy | Zendy; U. Dinesh Acharya | Zendy

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Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques

Author(s) -

G Shivaprasad,

N. V. Subba Reddy,

U. Dinesh Acharya

Publication year - 2015

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/19600-1451

Subject(s) - computer science , preprocessor , web mining , data pre processing , web log analysis software , knowledge extraction , data mining , task (project management) , information retrieval , data extraction , world wide web , web navigation , web service , artificial intelligence , static web page , medline , biology , biochemistry , management , economics

Web Usage Mining (WUM) refers to extraction of knowledge from the web log data by application of data mining techniques. WUM generally consists of Web Log Preprocessing, Web Log Knowledge Discovery and Web Log Pattern Analysis. Web Log Preprocessing is a major and complex task of WUM. Elimination of noise and irrelevant data, thereby reducing the burden on the system leads to efficient discovery of patterns by further stages of WUM. In this paper, Web Log Preprocessing Methods to efficiently identify users and user sessions have been implemented and results have been analyzed.

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