Median Graph Shift: A New Clustering Algorithm for Graph Domain

In the context of unsupervised clustering, a new algorithm for the domain of graphs is introduced. In this paper, the key idea is to adapt the mean-shift clustering and its variants proposed for the domain of feature vectors to graph clustering. These algorithms have been applied successfully in image analysis and computer vision domains. The proposed algorithm works in an iterative manner by shifting each graph towards the median graph in a neighborhood. Both the set median graph and the generalized median graph are tested for the shifting procedure. In the experiment part, a set of cluster validation indices are used to evaluate our clustering algorithm and a comparison with the well-known Kmeans algorithm is provided.

[1]  Ernest Valveny,et al.  An approximate algorithm for median graph computation using graph embedding , 2008, 2008 19th International Conference on Pattern Recognition.

[2]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[3]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Edwin R. Hancock,et al.  Spectral Feature Vectors for Graph Clustering , 2002, SSPR/SPR.

[5]  Ali Shokoufandeh,et al.  A Unified Framework for Indexing and Matching Hierarchical Shape Structures , 2001, IWVF.

[6]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[7]  Horst Bunke,et al.  Recent developments in graph matching , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[8]  Kaspar Riesen,et al.  Approximate graph edit distance computation by means of bipartite graph matching , 2009, Image Vis. Comput..

[9]  Vinciane Lacroix.  Automatic Palette Identification of Colored Graphics , 2009, GREC.

[10]  Vinciane Lacroix.  Raster-to-Vector Conversion: Problems and Tools Towards a Solution A Map Segmentation Application , 2009, 2009 Seventh International Conference on Advances in Pattern Recognition.

[11]  Ernest Valveny,et al.  Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph , 2009, CAIP.

[12]  L. A. Goodman,et al.  Measures of association for cross classifications , 1979 .

[13]  Horst Bunke,et al.  Validation indices for graph clustering , 2003, Pattern Recognit. Lett..

[14]  Salvatore Tabbone,et al.  A Hypergraph-Based Model for Graph Clustering: Application to Image Indexing , 2009, CAIP.

[15]  Takeo Kanade,et al.  Mode-seeking by Medoidshifts , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[16]  Horst Bunke,et al.  On Median Graphs: Properties, Algorithms, and Applications , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Mario Vento,et al.  Thirty Years Of Graph Matching In Pattern Recognition , 2004, Int. J. Pattern Recognit. Artif. Intell..

[18]  Kaspar Riesen,et al.  IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning , 2008, SSPR/SPR.