C-NBC: Neighborhood-Based Clustering with Constraints

Clustering is one of most important methods of data mining. It is used to identify unknown yet interesting and useful patterns or trends in datasets. There are different types of clustering algorithms such as partitioning, hierarchical, grid and density-based. In general, clustering methods are considered unsupervised, however, in recent years the new branch of clustering algorithms has emerged, namely constrained clustering algorithms. By means of socalled constraints, it is possible to incorporate background knowledge into clustering algorithms which usually leads to better performance and accuracy of clustering results. Through the last years, a number of clustering algorithms employing different types of constraints have been proposed and most of them extend existing partitioning and hierarchical approaches. Among density-based methods using constraints algorithms such as C-DBSCAN, DBCCOM, DBCluC were proposed. In this paper we offer a new C-NBC algorithm which combines known neighborhood-based algorithm (NBC) and instance-level constraints.