A new greedy algorithm for improving b-coloring clustering

Authors:
Haytham Elghazel;Tetsuya Yoshida;Véronique Deslandres;Mohand-Said Hacid;Alain Dussauchoy
Affiliations:
LIESP, ex. PRISMa Laboratory, Claude Bernard University of Lyon I, Villeurbanne cedex, France;Grad. School of Information Science and Technology, Hokkaido University, Sapporo, Japan;LIESP, ex. PRISMa Laboratory, Claude Bernard University of Lyon I, Villeurbanne cedex, France;LIRIS Laboratory, Claude Bernard University of Lyon I, Villeurbanne cedex, France;LIESP, ex. PRISMa Laboratory, Claude Bernard University of Lyon I, Villeurbanne cedex, France
Venue:
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Year:
2007

Citing 8
Cited 4

CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The b-chromatic number of a graph

Discrete Applied Mathematics
Data clustering: a review

ACM Computing Surveys (CSUR)
CLARANS: A Method for Clustering Objects for Spatial Data Mining

IEEE Transactions on Knowledge and Data Engineering
Clustering and its validation in a symbolic framework

Pattern Recognition Letters
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers
A new clustering approach for symbolic data and its validation: application to the healthcare data

ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

A graph-based projection approach for semi-supervised clustering

PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Toward improving re-coloring based clustering with graph b-coloring

PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A graph model for mutual information based clustering

Journal of Intelligent Information Systems
A re-coloring approach for graph b-coloring based clustering

International Journal of Knowledge-based and Intelligent Engineering Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new greedy algorithm to improve the specified b-coloring partition while satisfying b-coloring property. The b-coloring based clustering method in [3] enables to build a fine partition of the data set (classical or symbolic) into clusters even when the number of clusters is not pre-defined. It has several desirable clustering properties: utilization of topological relations between objects, robustness to outliers, all types of data can be accommodated, and identification of each cluster by at least one dominant object. However, it does not consider the high quality of the clusters in the construction of a b-coloring graph. The proposed algorithm in this paper can complement its weakness by re-coloring the objects to improve the quality of the constructed partition under the property and the dominance constraints. The proposed algorithm is evaluated against benchmark datasets and its effectiveness is confirmed.