Central Library

Normal view MARC view ISBD view

Advances in Web Mining and Web Usage Analysis [electronic resource] : 7th International Workshop on Knowledge Discovery on the Web, WEBKDD 2005, Chicago, IL, USA, August 21, 2005, Revised Papers / edited by Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu.

Contributor(s): Nasraoui, Olfa [editor.] | Zaiane, Osmar [editor.] | Spiliopoulou, Myra [editor.] | Mobasher, Manshad [editor.] | Masand, Brij [editor.] | Yu, Philip [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 4198Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006Edition: 1st ed. 2006.Description: X, 182 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540463481.Subject(s): Artificial intelligence | Computer networks  | Database management | Information storage and retrieval systems | Application software | Computers and civilization | Artificial Intelligence | Computer Communication Networks | Database Management | Information Storage and Retrieval | Computer and Information Systems Applications | Computers and SocietyAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Mining Significant Usage Patterns from Clickstream Data -- Using and Learning Semantics in Frequent Subgraph Mining -- Overcoming Incomplete User Models in Recommendation Systems Via an Ontology -- Data Sparsity Issues in the Collaborative Filtering Framework -- USER: User-Sensitive Expert Recommendations for Knowledge-Dense Environments -- Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation -- Adaptive Web Usage Profiling -- On Clustering Techniques for Change Diagnosis in Data Streams -- Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks.
In: Springer Nature eBookSummary: Thisbookcontainsthepostworkshopproceedingsofthe7thInternationalWo- shop on Knowledge Discovery from the Web, WEBKDD 2005. The WEBKDD workshop series takes place as part of the ACM SIGKDD International Conf- ence on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto theWeb.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. In the last years, the interest for the Web as medium for communication, interaction and business has led to new challenges and to intensive, dedicated research. Many of the infancy problems in Web mining have now been solved but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges.
    average rating: 0.0 (0 votes)
No physical items for this record

Mining Significant Usage Patterns from Clickstream Data -- Using and Learning Semantics in Frequent Subgraph Mining -- Overcoming Incomplete User Models in Recommendation Systems Via an Ontology -- Data Sparsity Issues in the Collaborative Filtering Framework -- USER: User-Sensitive Expert Recommendations for Knowledge-Dense Environments -- Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation -- Adaptive Web Usage Profiling -- On Clustering Techniques for Change Diagnosis in Data Streams -- Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks.

Thisbookcontainsthepostworkshopproceedingsofthe7thInternationalWo- shop on Knowledge Discovery from the Web, WEBKDD 2005. The WEBKDD workshop series takes place as part of the ACM SIGKDD International Conf- ence on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto theWeb.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. In the last years, the interest for the Web as medium for communication, interaction and business has led to new challenges and to intensive, dedicated research. Many of the infancy problems in Web mining have now been solved but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges.

There are no comments for this item.

Log in to your account to post a comment.