Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
From data mining to knowledge discovery: an overview
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The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
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
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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Establishing a lineage for medical knowledge discovery
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Visual discovery of network patterns of interaction between attributes in a data set 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. The approach complements analytical data mining techniques where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. For example, in the analysis of transaction data there are no known suspicious transactions. This chapter presents a human-centred visual data mining methodology that addresses the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. Different aspects of the approach is demonstrated through the reflection of the analytical process in two cases: one looking at fraudulent activity which will be difficult, if not impossible to detect with conventional exception detection methods, and the other one looking at exploring a large data set of low level communication data. 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.