Constrained graph b-coloring based clustering approach

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.