Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
MGV: A System for Visualizing Massive Multidigraphs
IEEE Transactions on Visualization and Computer Graphics
IEEE Intelligent Systems
An Overview of the GXL Graph Exchange Language
Revised Lectures on Software Visualization, International Seminar
FADE: Graph Drawing, Clustering, and Visual Abstraction
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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We present a prototype application for graph-based exploration and mining of online databases, with particular emphasis on scientific data. The application builds structured graphs that allow the user to explore patterns in a data set, including clusters, trends, outliers, and relationships. A number of different graphs can be rapidly generated, giving complementary insights into a given data set. The application has a Flash-based graphical interface and uses semantic information from the data sources to keep this interface as intuitive as possible. Data can be accessed from local and remote databases and files. Graphs can be explored using an interactive visual browser, or graph-analytic algorithms. We demonstrate the approach using marine sediment data, and show that differences in benthic species compositions in two Antarctic bays are related to heavy metal contamination.