Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Intelligent data analysis
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Summary from the KDD-03 panel: data mining: the next 10 years
ACM SIGKDD Explorations Newsletter
Engineering Contextual Information for Pervasive Multiagent Systems
Engineering Environment-Mediated Multi-Agent Systems
A new clustering algorithm based on data field in complex networks
The Journal of Supercomputing
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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 chapter presents a human-centred network data mining methodology that addresses the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. A case study from the area of security illustrates the application of the methodology and corresponding data mining techniques. The chapter argues that for many problems, a 'discovery' phase in the investigative process based on visualisation and human cognition is a logical precedent to, and complement of, more automated 'exception detection' phases.