The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, and an algorithm for solving the cluster analysis problem based on non-smooth optimization techniques is developed. We discuss applications of this algorithm in large databases. Results of numerical experiments are presented to demonstrate the effectiveness of this algorithm.C
Data mining is about solving problems by analyzing data that present in databases. Supervised and un...
Given a set of entities, Cluster Analysis aims at finding subsets, called clusters, which are homoge...
In this paper we discuss the solution of the clustering problem usually solved by the K-means algori...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
A popular apprach for solving complex optimization problems is through relaxation: some constraints ...
Clustering is one of an interesting data mining topics that can be applied in many fields. Recently,...
Cluster analysis deals with the problem of organization of a collection of objects into clusters bas...
The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An ...
Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex op...
The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex opt...
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
In this paper we discuss the solution of the clustering problem usually solved by the K-means algori...
Target of cluster analysis is to group data represented as a vector of measurements or a point in a ...
An algorithm is developed for solving clustering problems with the similarity measure defined using ...
Data mining is about solving problems by analyzing data that present in databases. Supervised and un...
Given a set of entities, Cluster Analysis aims at finding subsets, called clusters, which are homoge...
In this paper we discuss the solution of the clustering problem usually solved by the K-means algori...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
A popular apprach for solving complex optimization problems is through relaxation: some constraints ...
Clustering is one of an interesting data mining topics that can be applied in many fields. Recently,...
Cluster analysis deals with the problem of organization of a collection of objects into clusters bas...
The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An ...
Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex op...
The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex opt...
This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using ...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
In this paper we discuss the solution of the clustering problem usually solved by the K-means algori...
Target of cluster analysis is to group data represented as a vector of measurements or a point in a ...
An algorithm is developed for solving clustering problems with the similarity measure defined using ...
Data mining is about solving problems by analyzing data that present in databases. Supervised and un...
Given a set of entities, Cluster Analysis aims at finding subsets, called clusters, which are homoge...
In this paper we discuss the solution of the clustering problem usually solved by the K-means algori...