Risorsa Analitica di Monografia

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