Measuring Data Abstraction Quality in Multiresolution Visualizations
IEEE Transactions on Visualization and Computer Graphics
Clustering of document collection - A weighting approach
Expert Systems with Applications: An International Journal
A novel measure for validating clustering results applied to road traffic
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
A novel measure for validating clustering results applied to road traffic
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Multilevel compound tree: construction visualization and interaction
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Detecting communities in sparse MANETs
IEEE/ACM Transactions on Networking (TON)
A divergence-oriented approach for web users clustering
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Computer Science Review
Assessing the quality of multilevel graph clustering
Data Mining and Knowledge Discovery
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The aim of graph clustering is to define compact and well-separated clusters from a given graph.Cluster's compactness depends on datasets and clustering methods. In order to provide evaluation of graph clustering quality, many different indices have been proposed in previous work.Indices are used to compare different graph partitions but also different clustering techniques. Moreover, some clustering techniques are based on index optimization.Indices can also be added as visual tips in graph layouts.Despite the importance of the subject, little indices can not be easily compared or interpreted. In this paper, we provide a unified and synthetic view of indices used in graph clustering area and discuss them. We also propose several enhanced measures.