User-Driven Navigation Pattern Discovery from Internet Data

Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of the discovery of sequential access patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible navigation templates that can specify generic navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.

[1]  Maurice D. Mulvenna,et al.  Data-Driven Marketing , 1998, Electron. Mark..

[2]  David A. Bell,et al.  The role of domain knowledge in data mining , 1995, CIKM '95.

[3]  Mark Levene,et al.  Data Mining of User Navigation Patterns , 1999, WEBKDD.

[4]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[5]  Maurice D. Mulvenna,et al.  Discovering Internet marketing intelligence through online analytical web usage mining , 1998, SGMD.

[6]  Sarabjot Singh Anand,et al.  Decision support using data mining , 1998 .

[7]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[8]  Mohammed J. Zaki.  Efficient enumeration of frequent sequences , 1998, CIKM '98.

[9]  Myra Spiliopoulou,et al.  The Laborious Way From Data Mining to Web Log Mining , 1999 .

[10]  Heikki Mannila,et al.  Discovering Generalized Episodes Using Minimal Occurrences , 1996, KDD.

[11]  Myra Spiliopoulou,et al.  Data Mining for the Web , 1999, PKDD.

[12]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[13]  WebUsersMyra Spiliopoulou,et al.  A Data Miner analyzing the Navigational Behaviour of , 1999 .

[14]  Jiawei Han,et al.  Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases , 1994, KDD Workshop.

[15]  Philip S. Yu,et al.  Data mining for path traversal patterns in a web environment , 1996, Proceedings of 16th International Conference on Distributed Computing Systems.

[16]  Charles X. Ling,et al.  Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.

[17]  Jiawei Han,et al.  Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.