In this paper, we propose to adapt the batch version of selforganizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility...

In recent years, it has been realized that many problems in data mining can be seen as pure optimisation problems. In this work, we investigate the problem of constraint-based clustering from an optimisation point of view. The use of constraints in clustering is a recent development and allows to encode prior beliefs about desirable clusters. This paper proposes a new solution for minimum-sum-o...

Title of Thesis: Clustering with Propagated Constraints Eric Robert Eaton, Master of Science, 2005 Thesis directed by: Dr. Marie desJardins, Assistant Professor Department of Computer Science and Electrical Engineering Background knowledge in the form of constraints can dramatically improve the quality of generated clustering models. In constrained clustering, these constraints typically specif...

We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning techniqu...

Clustering with constraints is an important and developing area. However, most work is confined to conjunctions of simple together and apart constraints which limit their usability. In this paper, we propose a new formulation of constrained clustering that is able to incorporate not only existing types of constraints but also more complex logical combinations beyond conjunctions. We first show ...

We introduce a k−means type clustering in the presence of cannot–link and must–link constraints. First we apply a BIRCH type methodology to eliminate must–link constraints. Next we introduce a penalty function to substitute cannot–link constraints. When penalty values increase to +∞ the original cannot–link constraints are recovered. The preliminary numerical experiments show that constraints h...