Network Data Mining: Discovering Patterns of Interaction Between Attributes

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.