The Scent of a Newsgroup: Providing Personalized Access to Usenet Sites through Web Mining

We investigate Web mining techniques focusing on a specific application scenario: the problem of providing personalized access to Usenet sites accessible through a Web server. We analyze the data available from a Web-based Usenet server, and describe how traditional techniques for pattern discovery on the Web can be adapted to solve the problem of restructuring the access to news articles. In our framework, a personalized access tailored to the needs of each single user, can be devised according to both the content and the structure of the available data, and the past usage experience over such

[1]  MAGDALINI EIRINAKI,et al.  Web mining for web personalization , 2003, TOIT.

[2]  Elio Masciari,et al.  A framework for adaptive mail classification , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[3]  James E. Pitkow,et al.  Characterizing Browsing Behaviors on the World-Wide Web , 1995 .

[4]  Martin Ester,et al.  Frequent term-based text clustering , 2002, KDD.

[5]  Inderjit S. Dhillon,et al.  Concept Decompositions for Large Sparse Text Data Using Clustering , 2004, Machine Learning.

[6]  Hans-Peter Kriegel,et al.  Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.

[7]  Sourav S. Bhowmick,et al.  A survey of Web metrics , 2002, CSUR.

[8]  James E. Pitkow,et al.  Characterizing Browsing Strategies in the World-Wide Web , 1995, Comput. Networks ISDN Syst..

[9]  Jaideep Srivastava,et al.  Web usage mining: discovery and application of interesting patterns from web data , 2000 .

[10]  Jon M. Kleinberg,et al.  Inferring Web communities from link topology , 1998, HYPERTEXT '98.

[11]  Tao Luo,et al.  Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization , 2004, Data Mining and Knowledge Discovery.

[12]  Chaomei Chen,et al.  Mining the Web: Discovering knowledge from hypertext data , 2004, J. Assoc. Inf. Sci. Technol..

[13]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[14]  Geert Wets,et al.  A Framework for Self Adaptive Websites: Tactical versus Strategic Changes , 2000 .

[15]  Oren Etzioni,et al.  Adaptive Web Sites: Automatically Synthesizing Web Pages , 1998, AAAI/IAAI.

[16]  Riccardo Ortale,et al.  Similarity-based clustering of Web transactions , 2003, SAC '03.

[17]  R. Mooney,et al.  Impact of Similarity Measures on Web-page Clustering , 2000 .

[18]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.

[19]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[20]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[21]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[22]  Tao Luo,et al.  Integrating Web Usage and Content Mining for More Effective Personalization , 2000, EC-Web.

[23]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.

[24]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[25]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[26]  Sudipto Guha,et al.  ROCK: A Robust Clustering Algorithm for Categorical Attributes , 2000, Inf. Syst..

[27]  Ramana Rao,et al.  Silk from a sow's ear: extracting usable structures from the Web , 1996, CHI.

[28]  Padhraic Smyth,et al.  A general probabilistic framework for clustering individuals and objects , 2000, KDD '00.

[29]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[30]  Fosca Giannotti,et al.  Clustering Transactional Data , 2002, PKDD.

[31]  Ada Wai-Chee Fu,et al.  Incremental Document Clustering for Web Page Classification , 2002 .

[32]  Ravi Kumar,et al.  Self-similarity in the web , 2001, TOIT.

[33]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[34]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[35]  Tao Luo,et al.  Using sequential and non-sequential patterns in predictive Web usage mining tasks , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[36]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[37]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[38]  Marie-Francine Moens,et al.  Automatic Indexing and Abstracting of Document Texts , 2000, Computational Linguistics.

[39]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[40]  Yen-Jen Oyang,et al.  An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory , 2002, PAKDD.

[41]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[42]  Tao Luo,et al.  Effective personalization based on association rule discovery from web usage data , 2001, WIDM '01.

[43]  Andrés Iglesias.  A New Framework for Intelligent Semantic Web Services Based on GAIVAs , 2008, Int. J. Inf. Technol. Web Eng..

[44]  Oren Etzioni,et al.  Towards adaptive Web sites: Conceptual framework and case study , 1999, Artif. Intell..

[45]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[46]  Pat Langley,et al.  Models of Incremental Concept Formation , 1990, Artif. Intell..

[47]  Surithong Srisa‐ard,et al.  Mining the Web: Discovering Knowledge from Hypertext Data , 2003 .