Inhaltsangabe
This book presents methods, approaches and techniques to perform the main tasks of Web log preprocessing that include Data Cleaning, Web user identification and extraction of user sessions. In order to accomplish the dimensionality reduction and trim down the adverse effect of outliers, a fuzzy set theoretic approach for assigning fuzzy weights to user sessions and associated URLs has been described. Another main part discussed in this book is the analysis of the Web server log file preprocessing results and discovery of user session clusters using Fuzzy c-Means algorithm. Mainly this book is suitable for researchers who are interested in the preprocessing techniques of web log data for Web usage mining applications such as Web personalization, website design, business intelligence etc.
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Reseña del editor
This book presents methods, approaches and techniques to perform the main tasks of Web log preprocessing that include Data Cleaning, Web user identification and extraction of user sessions. In order to accomplish the dimensionality reduction and trim down the adverse effect of outliers, a fuzzy set theoretic approach for assigning fuzzy weights to user sessions and associated URLs has been described. Another main part discussed in this book is the analysis of the Web server log file preprocessing results and discovery of user session clusters using Fuzzy c-Means algorithm. Mainly this book is suitable for researchers who are interested in the preprocessing techniques of web log data for Web usage mining applications such as Web personalization, website design, business intelligence etc.
Biografía del autor
Dr. Zahid Ansari is a Professor of CSE and Dean (Research) at PA College of Engineering, Mangaluru, India. Earlier he was associated with Tata Consultancy Services R&D Center, Pune, BARC Mumbai, GE-Harris Melbourne Florida USA and USAA, San Antonio Texas USA. He has published 55+ articles in various international journals and conferences of repute.
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Web Log Preprocessing and Fuzzy Pattern Discovery
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 6202050233
ISBN 13: 9786202050234
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