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Network Data Mining: Discovering Patterns of Interaction Between Attributes

  • John Galloway
  • Simeon J. Simoff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Network Data Mining identifies emergent networks between myriads of individual data items and utilises special statistical algorithms that aid visualisation of ‘emergent’ patterns and trends in the linkage. It complements predictive data mining methods and methods for outlier detection, which assume the independence between the attributes and the independence between the values of these attributes. Many problems, however, especially phenomena of a more complex nature, are not well suited for these methods. For example, in the analysis of transaction data there are no known suspicious transactions. This paper presents a human-centred methodology and supporting techniques that address the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. The methodology and corresponding techniques are illustrated on a case study from the area of security.

Keywords

Visual Model Linkage Pattern Reflective Practitioner Emergent Group Implicit Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Galloway
    • 1
    • 2
  • Simeon J. Simoff
    • 3
    • 4
  1. 1.Complex Systems Research CentreUniversity of Technology SydneyBroadwayAustralia
  2. 2.Chief ScientistNetMap Analytics Pty LtdSt LeonardsAustralia
  3. 3.Faculty of Information TechnologyUniversity of Technology SydneyBroadwayAustralia
  4. 4.Electronic Markets GroupInstitute for Information and Communication TechnologiesAustralia

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