Add to ORKGThe minimum-sum-of-squared error clustering (MSSC) is one of the most intuitive and popular clustering algorithms. In this paper, we first show that MSSC can be equivalently cast as a concave minimization prob-lem. To find the global solution of MSSC, we construct a procedure to move from a fractional solution of the relaxed minimization problem to an integer solution with improved objective value. Then we adapt Tuy’s convexity cut method, a powerful algorithm for global concave optimiza-tion, to find a global optimum to MSSC. Promising numerical examples are reported.
The main purpose of this dissertation is to demonstrate that using a robust loss function (instead o...
This paper discusses various extensions of the classical within-group sum of squared errors function...
The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex opt...
In this paper, we adapt Tuy's concave cutting plane method to the problem of finding an optimal grou...
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
Minimum sum-of-squares clustering (MSSC) consists in partitioning a given set of n points into k clu...
The minimum sum-of-squares clustering problem is a very important problem in data mining and machine...
Fast accumulation of large amounts of complex data has created a needfor more sophisticated statisti...
Clustering is an important task in data mining. It can be formulated as a global optimization proble...
Abstract: Fast accumulation of large amounts of complex data has cre-ated a need for more sophistica...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
A finite new algorithm is proposed for clustering m given points in n-dimensional real space into k ...
The minimum sum-of-squares clustering problem (MSSC) consists of partitioning n observations into k...
In this paper, we survey the usage of semidefinite programming (SDP), and nonsmooth optimization app...
The main purpose of this dissertation is to demonstrate that using a robust loss function (instead o...
This paper discusses various extensions of the classical within-group sum of squared errors function...
The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex opt...
In this paper, we adapt Tuy's concave cutting plane method to the problem of finding an optimal grou...
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
Minimum sum-of-squares clustering (MSSC) consists in partitioning a given set of n points into k clu...
The minimum sum-of-squares clustering problem is a very important problem in data mining and machine...
Fast accumulation of large amounts of complex data has created a needfor more sophisticated statisti...
Clustering is an important task in data mining. It can be formulated as a global optimization proble...
Abstract: Fast accumulation of large amounts of complex data has cre-ated a need for more sophistica...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
A finite new algorithm is proposed for clustering m given points in n-dimensional real space into k ...
The minimum sum-of-squares clustering problem (MSSC) consists of partitioning n observations into k...
In this paper, we survey the usage of semidefinite programming (SDP), and nonsmooth optimization app...
The main purpose of this dissertation is to demonstrate that using a robust loss function (instead o...
This paper discusses various extensions of the classical within-group sum of squared errors function...
The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex opt...