Discretization of Multidimensional Web Data for Informative Dense Regions Discovery

Edmond H. Wu, Michael K. Ng, Andy M. Yip, Tony F. Chan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)718-724
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2004
Externally publishedYes


  • Dense regions discovery
  • Discretization
  • Web information system
  • Web mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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