C-NBC: Neighborhood-Based Clustering with Constraints.
Piotr Lasek · 2014
Abstract. Clustering is one of most important methods of data mining. It is used to identify unknown yet interesting and useful patterns or trends in da-tasets. There are different types of clustering algorithms such as partitioning, hierarchical, grid and density-based. In general, clustering methods are consid-ered unsupervised, however, in recent years the new branch of clustering algo-rithms has emerged, namely constrained clustering algorithms. By means of so-called constraints, it is possible to incorporate background knowledge into clus-tering 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.