AbstractWe would like to classify the vertices of a hypergraph in the way that ‘similar’ vertices (those having many incident edges in common) belong to the same cluster. The problem is formulated as follows: given a connected hypergraph on n vertices and fixing the integer k(1<k⩽n), we are looking for k-partition of the set of vertices such that the edges of the corresponding cut-set be as few as possible. We introduce some combinatorial measures characterizing this structural property and give upper and lower bounds for them by means of the k smallest eigenvalues of the hypergraph. For this purpose the notion of spectra of hypergraphs — which is the generalization of C-spectra of graphs — is also introduced together with k-dimensional Euc...
Abstract—Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we pr...
International audienceHypergraphs are generalization of graphs where each edge (hyperedge) can conne...
In this work we study the widely used spectral clustering algorithms, i.e. partition a graph into k ...
International audienceIn the last few years, hypergraph-based methods have gained considerable atten...
The images of an object may look very different under different illumination conditions or viewing d...
2 3Abstract: This is a survey of the method of normalized graph cuts and its applications to graph c...
Classification problems of the vertices of large multigraphs (hypergraphs or weighted graphs) can be...
We define a general variant of the graph clustering problem where the criterion of density for the c...
The goal of the graph partitioning problem is to find groups such that entities within the same grou...
We consider the partitioning of m-dimensional lattice graphs using Fiedler’s approach [1], that requ...
These are notes on the method of normalized graph cuts and its applications to graph clustering. I p...
We consider the partitioning of m-dimensional lattice graphs using Fiedler’s approach [1], that requ...
Clustering of data in a large dimension space is of a great interest in many data mining application...
AbstractMiller, Teng, Thurston, and Vavasis proved a geometric separator theorem which implies that ...
This course project provide the basic theory of spectral clustering from a graph partitioning point ...
Abstract—Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we pr...
International audienceHypergraphs are generalization of graphs where each edge (hyperedge) can conne...
In this work we study the widely used spectral clustering algorithms, i.e. partition a graph into k ...
International audienceIn the last few years, hypergraph-based methods have gained considerable atten...
The images of an object may look very different under different illumination conditions or viewing d...
2 3Abstract: This is a survey of the method of normalized graph cuts and its applications to graph c...
Classification problems of the vertices of large multigraphs (hypergraphs or weighted graphs) can be...
We define a general variant of the graph clustering problem where the criterion of density for the c...
The goal of the graph partitioning problem is to find groups such that entities within the same grou...
We consider the partitioning of m-dimensional lattice graphs using Fiedler’s approach [1], that requ...
These are notes on the method of normalized graph cuts and its applications to graph clustering. I p...
We consider the partitioning of m-dimensional lattice graphs using Fiedler’s approach [1], that requ...
Clustering of data in a large dimension space is of a great interest in many data mining application...
AbstractMiller, Teng, Thurston, and Vavasis proved a geometric separator theorem which implies that ...
This course project provide the basic theory of spectral clustering from a graph partitioning point ...
Abstract—Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we pr...
International audienceHypergraphs are generalization of graphs where each edge (hyperedge) can conne...
In this work we study the widely used spectral clustering algorithms, i.e. partition a graph into k ...