An Efficient Algorithm for Graph Isomorphism
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
Bunch: A Clustering Tool for the Recovery and Maintenance of Software System Structures
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Motif Search in Graphs: Application to Metabolic Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Engineering graph clustering: Models and experimental evaluation
Journal of Experimental Algorithmics (JEA)
Internal quality measures for clustering in metric spaces
International Journal of Business Intelligence and Data Mining
Multiscale visualization of small world networks
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Computer Science Review
Improving multiple aesthetics produces better graph drawings
Journal of Visual Languages and Computing
Hi-index | 0.00 |
Many real world systems can be modeled as networks or graphs. Clustering algorithms that help us to organize and understand these networks are usually referred to as, graph based clustering algorithms. Many algorithms exist in the literature for clustering network data. Evaluating the quality of these clustering algorithms is an important task addressed by different researchers. An important ingredient of evaluating these clustering techniques is the node-edge density of a cluster. In this paper, we argue that evaluation methods based on density are heavily biased to networks having dense components, such as social networks, but are not well suited for data sets with other network topologies where the nodes are not densely connected. Example of such data sets are the transportation and Internet networks. We justify our hypothesis by presenting examples from real world data sets. We present a new metric to evaluate the quality of a clustering algorithm to overcome the limitations of existing cluster evaluation techniques. This new metric is based on the path length of the elements of a cluster and avoids judging the quality based on cluster density. We show the effectiveness of the proposed metric by comparing its results with other existing evaluation methods on artificially generated and real world data sets.