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

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 111 - Number 13
Year of Publication: 2015
Authors:
Shivaprasad G.
N. V. Subba Reddy
U. Dinesh Acharya
10.5120/19600-1451

Shivaprasad G., Subba N v Reddy and Dinesh U Acharya. Article: Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques. International Journal of Computer Applications 111(13):27-32, February 2015. Full text available. BibTeX

@article{key:article,
	author = {Shivaprasad G. and N.v. Subba Reddy and U. Dinesh Acharya},
	title = {Article: Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {111},
	number = {13},
	pages = {27-32},
	month = {February},
	note = {Full text available}
}

Abstract

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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