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INCDM
2010
Springer

Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths

10 years 10 months ago
Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
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 ex...
Faraz Zaidi, Daniel Archambault, Guy Melanç
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2010
Where INCDM
Authors Faraz Zaidi, Daniel Archambault, Guy Melançon
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