Network data mining: methods and techniques for discovering deep linkage between attributes

Network Data Mining identifies emergent networks between myriads of individual data items and utilises special algorithms that aid visualisation of `emergent' patterns and trends in the linkage. It complements conventional data mining methods, which assume the independence between the attributes and the independence between the values of these attributes. These techniques typically flag, alert or alarm instances or events that could represent anomalous behaviour or irregularities because of a match with pre-defined patterns or rules. They serve as `exception detection' methods where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. Many problems are suited to this approach. Many problems however, especially those of a more complex nature, are not well suited. The rules or definitions simply cannot be specified. For example, in the analysis of transaction data there are no known suspicious transactions. This chapte...

• Type: Conference
• Year: 2006
• Where: APCCM
• Authors: John Galloway, Simeon J. Simoff
• Keywords: APCCM 2006, APCCM 2007, Data Mining, Data Mining Methods, Network Data Mining