What can we do with graph-structured data? – a data mining perspective

Authors:
Hiroshi Motoda
Affiliations:
Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

Recent advancement of data mining techniques has made it possible to mine from complex structured data. Since structure is represented by proper relations and a graph can easily represent relations, knowledge discovery from graph-structured data (graph mining) poses a general problem for mining from structured data. Some examples amenable to graph mining are finding functional components from their behavior, finding typical web browsing patterns, identifying typical substructures of chemical compounds, finding typical subsequences of DNA and discovering diagnostic rules from patient history records. These are based on finding some typicality from a vast amount of graph-structured data.