%0 Book Section %1 liu2011usage %A Liu, Bing %A Mobasher, Bamshad %A Nasraoui, Olfa %B Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data %C Berlin, Heidelberg %D 2011 %I Springer Berlin Heidelberg %K mining usage web %P 527--603 %R 10.1007/978-3-642-19460-3_12 %T Web Usage Mining %U http://dx.doi.org/10.1007/978-3-642-19460-3_12 %X With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the life-time value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources. %@ 978-3-642-19460-3

@inbook{liu2011usage, abstract = {With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the life-time value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources.}, added-at = {2018-04-30T22:37:45.000+0200}, address = {Berlin, Heidelberg}, author = {Liu, Bing and Mobasher, Bamshad and Nasraoui, Olfa}, biburl = {https://www.bibsonomy.org/bibtex/269bae5a657130439d394305f034b069c/daswesen}, booktitle = {Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data}, description = {Web Usage Mining - Springer}, doi = {10.1007/978-3-642-19460-3_12}, interhash = {b04acc27f6fb9b7f2b439e9b277a2da4}, intrahash = {69bae5a657130439d394305f034b069c}, isbn = {978-3-642-19460-3}, keywords = {mining usage web}, pages = {527--603}, publisher = {Springer Berlin Heidelberg}, timestamp = {2018-04-30T22:37:45.000+0200}, title = {Web Usage Mining}, url = {http://dx.doi.org/10.1007/978-3-642-19460-3_12}, year = 2011 }