Instance-Level Constraints in Density-Based Clustering

Clustering data into meaningful groups is one of most important tasks of both artificial intelligence and data mining. In general, clustering methods are considered unsupervised. However, in recent years, so-named constraints become more popular as means of incorporating additional knowledge into clustering algorithms. Over the last years, a number of clustering algorithms employing different types of constraints have been proposed. In this paper we focus on instance level constraints such as must-link and cannot-link and present theoretical considerations on employing them in a well know density-based clustering algorithm – DBSCAN. Additionally we present a modified version of the algorithm using the discussed type of constraints.