The b-chromatic number of a graph
Discrete Applied Mathematics
ACM Computing Surveys (CSUR)
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
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
A Probabilistic Approach for Constrained Clustering with Topological Map
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Constraint selection for semi-supervised topological clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Towards decision support for a home care services platform
Proceedings of the 4th International Workshop on Web Intelligence & Communities
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Clustering is generally defined as an unsupervised data mining process which aims to divide a set of data into groups, or clusters, such that the data within the same group are similar to each other while data from different groups are dissimilar. However, additional background information (namely constraints) are available in some domains and must be considered in the clustering solutions. Recently, we have developed a new graph b-coloring clustering algorithm. It exhibits more important clustering features and enables to build a fine partition of the data set in clusters when the number of clusters is not pre-defined. In this paper, we propose an extension of this method to incorporate two types of Instance-Level clustering constraints (must-link and cannot-link constraints). In experiments with artificial constraints on benchmark data sets, we show improvements in the quality of the clustering solution and the computational complexity of the algorithm.