Support vector based spherical clustering is described as an optimization problem posed in the input space where the cluster indicators are also considered as variables. The robust clustering is attempted to be found by taking the objective function of the optimization problem as energy function of the gradient network. The proposed method is an extension of the work by the authors formulating the clustering problem as a mixed integer optimization by considering the cluster indicators and centers as variables
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
We present a novel clustering method using the approach of support vector machines. Data points are...
This work contains several theoretical and numerical studies on data clustering. The total squared e...
A spherical clustering algorithm that provides robustness against noise and outliers is proposed. It...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
In this paper, two different optimization formulations for clustering problem are considered. The fi...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
The objective functions in optimization models of the sum-of-squares clustering problem reflect intr...
In this paper, we survey the usage of semidefinite programming (SDP), and nonsmooth optimization app...
In this paper we examine some of the relationships between two important optimization problems that ...
The conventional robust method for clustering arbitrarily-shaped clusters takes a long time to proce...
We present a novel method for clustering using the support vector machine approach. Data points are ...
Abstract. We discuss a variety of clustering problems arising in combinatorial pplications and in cl...
Ces travaux traitent de la problématique du partitionnement d'un ensemble d'observations ou de varia...
Target of cluster analysis is to group data represented as a vector of measurements or a point in a ...
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
We present a novel clustering method using the approach of support vector machines. Data points are...
This work contains several theoretical and numerical studies on data clustering. The total squared e...
A spherical clustering algorithm that provides robustness against noise and outliers is proposed. It...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
In this paper, two different optimization formulations for clustering problem are considered. The fi...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
The objective functions in optimization models of the sum-of-squares clustering problem reflect intr...
In this paper, we survey the usage of semidefinite programming (SDP), and nonsmooth optimization app...
In this paper we examine some of the relationships between two important optimization problems that ...
The conventional robust method for clustering arbitrarily-shaped clusters takes a long time to proce...
We present a novel method for clustering using the support vector machine approach. Data points are ...
Abstract. We discuss a variety of clustering problems arising in combinatorial pplications and in cl...
Ces travaux traitent de la problématique du partitionnement d'un ensemble d'observations ou de varia...
Target of cluster analysis is to group data represented as a vector of measurements or a point in a ...
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
We present a novel clustering method using the approach of support vector machines. Data points are...
This work contains several theoretical and numerical studies on data clustering. The total squared e...