This paper continues the investigation of semigroup constructions motivated by applications in data mining. We give a complete description of the error-correcting capabilities of a large family of clusterers based on Rees matrix semigroups well known in semigroup theory. This result strengthens and complements previous formulas recently obtained in the literature. Examples show that our theorems do not generalize to other classes of semigroups
Cluster ensembles are collections of individual solutions to a given clustering problem which are us...
monothetic divisive algorithms often need for some means of post-classification relocation. We intro...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
The aim of the present article is to obtain a theoretical result essential for applications of combi...
Abstract We introduce a new construction involving Rees matrix semigroups and max-plus algebras that...
The present article continues the investigation of constructions essential for applications of combi...
A general approach to designing multiple classifiers represents them as a combination of several bin...
Max-plus algebras and more general semirings have many useful applications and have been actively in...
Effective multiple clustering systems, or clusterers, have important applications in information sec...
Many semi-supervised clustering algorithm-s have been proposed to improve the clus-tering accuracy b...
Clustering seeks to group or to lump together objects or variables that share some observed qualitie...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
We introduce the problem of cluster-grouping and show that it can be considered a subtask in several...
Data mining has been a significant tool in extracting hidden and useful information from large datab...
Semisupervised clustering extends standard clustering methods to the semisupervised setting, in some...
Cluster ensembles are collections of individual solutions to a given clustering problem which are us...
monothetic divisive algorithms often need for some means of post-classification relocation. We intro...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
The aim of the present article is to obtain a theoretical result essential for applications of combi...
Abstract We introduce a new construction involving Rees matrix semigroups and max-plus algebras that...
The present article continues the investigation of constructions essential for applications of combi...
A general approach to designing multiple classifiers represents them as a combination of several bin...
Max-plus algebras and more general semirings have many useful applications and have been actively in...
Effective multiple clustering systems, or clusterers, have important applications in information sec...
Many semi-supervised clustering algorithm-s have been proposed to improve the clus-tering accuracy b...
Clustering seeks to group or to lump together objects or variables that share some observed qualitie...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
We introduce the problem of cluster-grouping and show that it can be considered a subtask in several...
Data mining has been a significant tool in extracting hidden and useful information from large datab...
Semisupervised clustering extends standard clustering methods to the semisupervised setting, in some...
Cluster ensembles are collections of individual solutions to a given clustering problem which are us...
monothetic divisive algorithms often need for some means of post-classification relocation. We intro...
ia that provide significant distinctions between clustering methods and can help selecting appropria...