Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph

Authors:
M. Ferrer;E. Valveny;F. Serratosa;I. Bardají;H. Bunke
Affiliations:
Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain 08028;Centre de Visió per Computador, Universitat Autònoma de Barcelona, Bellaterra, Spain 08193;Departament d'Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain 43007;Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain 08028;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland CH-3012
Venue:
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Year:
2009

Citing 0
Cited 5

Large sample statistics in the domain of graphs

SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Graph quantization

Computer Vision and Image Understanding
A generic framework for median graph computation based on a recursive embedding approach

Computer Vision and Image Understanding
Pattern analysis with graphs: Parallel work at Bern and York

Pattern Recognition Letters
Generalized median string computation by means of string embedding in vector spaces

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.