Add to ORKGThe field of pairwise clustering is currently domi-nated by the idea of dividing a set of objects into dis-joints classes, thereby giving rise to (hard) partitions of the input data. However, in many computer vision and pattern recognition problems this approach is too restrictive as objects might reasonably belong to more than one class. In this paper, we adopt a game-theoretic perspective to the iterative extraction of possibly over-lapping clusters: Game dynamics are used to locate in-dividual groups, and after each extraction the similarity matrix is transformed in such a way as to make the lo-cated cluster unstable under the dynamics, without af-fecting the remaining groups.
Pairwise (or graph-based) clustering algorithms typically assume the existence of a single affinity ...
Data clustering considers the problem of grouping data into clusters based on its similarity measure...
We develop a framework for the image segmentation problem based on a new graph-theoretic formulation...
Clustering refers to the process of extracting maximally coherent groups from a set of objects using...
Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of ob...
Clustering is a technique for discovering patterns and structure in data. Often, the most difficult ...
In this paper, we approach the classical problem of clustering using solution concepts from cooperat...
Clustering refers to the process of extracting maximally coherent groups from a set of objects using...
We develop a new graph-theoretic approach for pairwise data clustering which is motivated by the ana...
The problem of clustering consists in organizing a set of objects into groups or clusters, in a way ...
In this paper, we develop a game theoretic approach for clustering features in a learning problem. F...
Recently dominant sets, a generalization of the notion of the maximal clique to edge-weighted graphs...
Pairwise grouping and clustering approaches have tra-ditionally worked under the assumption that the...
Abstract. Recently, a novel graph-theoretic notion of a cluster has been proposed, i.e., the “domina...
Pairwise (or graph-based) clustering algorithms typically assume the existence of a single affinity ...
Pairwise (or graph-based) clustering algorithms typically assume the existence of a single affinity ...
Data clustering considers the problem of grouping data into clusters based on its similarity measure...
We develop a framework for the image segmentation problem based on a new graph-theoretic formulation...
Clustering refers to the process of extracting maximally coherent groups from a set of objects using...
Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of ob...
Clustering is a technique for discovering patterns and structure in data. Often, the most difficult ...
In this paper, we approach the classical problem of clustering using solution concepts from cooperat...
Clustering refers to the process of extracting maximally coherent groups from a set of objects using...
We develop a new graph-theoretic approach for pairwise data clustering which is motivated by the ana...
The problem of clustering consists in organizing a set of objects into groups or clusters, in a way ...
In this paper, we develop a game theoretic approach for clustering features in a learning problem. F...
Recently dominant sets, a generalization of the notion of the maximal clique to edge-weighted graphs...
Pairwise grouping and clustering approaches have tra-ditionally worked under the assumption that the...
Abstract. Recently, a novel graph-theoretic notion of a cluster has been proposed, i.e., the “domina...
Pairwise (or graph-based) clustering algorithms typically assume the existence of a single affinity ...
Pairwise (or graph-based) clustering algorithms typically assume the existence of a single affinity ...
Data clustering considers the problem of grouping data into clusters based on its similarity measure...
We develop a framework for the image segmentation problem based on a new graph-theoretic formulation...