Discretization of multidimensional web data for informative dense regions discovery

Authors:
Edmond H. Wu;Michael K. Ng;Andy M. Yip;Tony F. Chan
Affiliations:
Department of Mathematics, The University of Hong Kong, Hong Kong;Department of Mathematics, The University of Hong Kong, Hong Kong;Department of Mathematics, University of California, Los Angeles, CA;Department of Mathematics, University of California, Los Angeles, CA
Venue:
CIS'04 Proceedings of the First international conference on Computational and Information Science
Year:
2004

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Abstract

Dense regions discovery is an important knowledge discovery process for finding distinct and meaningful patterns from given data. The challenge in dense regions discovery is how to find informative patterns from various types of data stored in structured or unstructured databases, such as mining user patterns from Web data. Therefore, novel approaches are needed to integrate and manage these multi-type data repositories to support new generation information management systems. In this paper, we focus on discussing and purposing several discretization methods for large matrices. The experiments suggest that the discretization methods can be employed in practical Web applications, such as user patterns discovery.