EFFICIENT WEB USAGE MINING BASED ON FORMAL CONCEPT ANALYSIS

Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the web. Web usage mining has become very critical for effective web site management, creating adaptive web sites, business and support services, personalization and so on. Web usage mining aims to discover interesting user access patterns from web logs. Formal Based Concept Analysis(FBCA) is an effective data analysis technique based on ordered lattice theory. Formal Based Concept can then be generated and interpreted from the concept lattice using FBCA.FBCA has been applied to a wide range of domains including conceptual clustering, information retrieval and knowledge discovery. In this paper, we propose a novel FBCA approach for web usage mining. In our approach, the FBCA technique is applied to mine association rules from web usage lattice constructed from web logs. The discovered knowledge(association rules) can then be used for practical web applications such as web recommendation and personalization. We apply the FBCA-mined association rules to web recommendation and compare its performance with that of classical Apriority -mined rules. The results indicate that the proposed FBCA approach not only generates far fewer rules than Apriority-based algorithms, the generated rules are also of comparable quality with respect to three objective performance measures.

[1]  Jong-Seok. Kim.  Mining association rules using formal concept analysis. , 2002 .

[2]  L. Wood,et al.  From the Authors , 2003, European Respiratory Journal.

[3]  StummeGerd,et al.  Computing iceberg concept lattices with TITANIC , 2002 .

[4]  Rokia Missaoui,et al.  An Incremental Concept Formation Approach for Learning from Databases , 1994, Theor. Comput. Sci..

[5]  Gerd Stumme,et al.  Conceptual Clustering with Iceberg Concept Lattices , 2001 .

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

[7]  Yves Bastide,et al.  Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis , 2001, KI/ÖGAI.

[8]  Paul Compton,et al.  Formal Concept Analysis for Domain-Specific Document Retrieval Systems , 2001, Australian Joint Conference on Artificial Intelligence.

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

[10]  Nicolas Pasquier.  Mining Association Rules using Formal Concept Analysis , 2000 .

[11]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[12]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[13]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[14]  Rudolf Wille,et al.  Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts , 2009, ICFCA.

[15]  Gerd Stumme,et al.  Conceptual Knowledge Discovery and Data Analysis , 2000, ICCS.

[16]  Gerd Stumme,et al.  Efficient Data Mining Based on Formal Concept Analysis , 2002, DEXA.

[17]  Rokia Missaoui,et al.  Building Concept (Galois) Lattices from Parts: Generalizing the Incremental Methods , 2001, ICCS.

[18]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[19]  Lhouari Nourine,et al.  A Fast Algorithm for Building Lattices , 1999, Inf. Process. Lett..