We consider the problem of spectral clustering with partial supervision in the form of must-link and cannot-link constraints. Such pairwise constraints are common in problems like coreference resolution in natural language processing. The approach developed in this paper is to learn a new representation space for the data together with a distance in this new space. The representation space is o...

We consider the problem of clustering a given dataset into k clusters subject to an additional set of constraints on relative distance comparisons between the data items. The additional constraints are meant to reflect side-information that is not expressed in the feature vectors, directly. Relative comparisons can express structures at finer level of detail than must-link (ML) and cannot-link ...

This article proposes a constrained clustering algorithmwith competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learn...

In this paper we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of user queries to define a set of must-link or cannot-link constraints. Some works on active constraint learning have already been proposed but they are mainly applied to K-Means like clustering algorithms which are known to be limited to spherical c...

In this paper, we address the problem of constrained clustering along with active selection of clustering constraints in a unified framework. To this aim, we extend the improved possibilistic c-Means algorithm (IPCM) with a multiple kernels learning setting under supervision of side information. By incorporating multiple kernels, the limitation of improved possibilistic c-means to spherical clu...

The problem of clustering with constraints has received considerable attention in the last decade. Indeed, several algorithms have been proposed, but only a few studies have (partially) compared their performances. In this work, three well-known algorithms for k-means-based clustering with soft constraints — Constrained Vector Quantization Error (CVQE), its variant named LCVQE, and the Metric P...

When confronted to a clustering problem, one has to choose which algorithm to run. Building a system that automatically chooses an algorithm for a given task is the algorithm selection problem. Unlike the well-studied task of classification, clustering algorithm selection cannot rely on labels to choose which algorithm to use. However, in the context of constraint-based clustering, we argue tha...

Using background knowledge in clustering, called semi-clustering, is one of the actively researched areas in data mining. In this paper, we illustrate how to use background knowledge related to a domain more efficiently. For a given data, the number of classes is investigated by using the must-link constraints before clustering and these must-link data are assigned to the corresponding classes....

The self-organizing map (SOM) network was originally designed for solving problems that involve tasks such as clustering, visualization, and abstraction. While Kohonen’s SOM networks have been successfully applied as a classi6cation tool to various problem domains, their potential as a robust substitute for clustering and visualization analysis remains relatively unresearched. We believe the in...

We investigate a co-clustering framework (i.e., a method that provides a partition of objects and a linked partition of features) for binary data sets. So far, constrained co-clustering has been seldomly explored. First, we consider straightforward extensions of the classical instance level constraints (Must-link, Cannot-link) to express relationships on both objects and features. Furthermore, ...