Abstract : We describe a simple, fast computing and easy to imple- ment method for finding relatively good clusterings of soft- ware systems. Our method relies on the ability to compute the strength of an edge in a graph by applying a straight- forward metric defined in terms of the neighborhoods of its end vertices. The metric is used to identify the weak edges of the graph, which are momentarily deleted to break it into several components. We study the quality metric M Q intro- duced in [1] and exhibit mathematical properties that make it a good measure for clustering quality. Letting the thresh- old weakness of edges vary defines a path, i.e. a sequence of clusterings in the solution space (of all possible cluster- ing of the graph). This path is described in terms of a curve linking MQ to the weakness of the edges in the graph.
https://hal-lirmm.ccsd.cnrs.fr/lirmm-00269464
Contributor : Christine Carvalho de Matos
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Submitted on : Thursday, April 3, 2008 - 8:12:12 AM
Last modification on : Tuesday, March 19, 2019 - 1:26:04 PM
Long-term archiving on : Friday, September 28, 2012 - 12:10:34 PM
Yves Chiricota, Fabien Jourdan, Guy Melançon. Software Components Capture using Graph Clustering. IEEE International Workshop on Program Comprehension, 2003, Portland, Oregon, United States. pp.217-226. ⟨lirmm-00269464⟩
