Add to ORKGProducción CientíficaA new method for performing robust clustering is proposed. The method is designed with the aim of ¯tting clusters with di®erent scat- ters and weights. A proportion ® of contaminating data points is also allowed. Restrictions on the ratio between the maximum and the min- imum eigenvalues of the groups scatter matrices are introduced. These restrictions make the problem to be well-de¯ned guaranteeing the ex- istence and the consistency of the sample estimators to the population parameters.Estadística e I
The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and...
We assess the performance of state-of-the-art robust clustering tools for regression structures unde...
Trimming principles play an important role in robust statistics. However, their use for clustering ...
A new method for performing robust clustering is proposed. The method is designed with the aim of fi...
TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-m...
Outliers can be extremely harmful when applying well-known Cluster Analysis methods. More- over, clu...
Outliers can be extremely harmful when applying well-known Cluster Analysis methods. More- over, clu...
Producción CientíficaTwo key questions in Clustering problems are how to determine the number of gr...
An iteratively reweighted approach for robust clustering is presented in this work. The method is i...
Outlying data can heavily influence standard clustering methods. At the same time, clustering princi...
An iteratively reweighted approach for robust clustering is presented in this work. The method is i...
An iteratively reweighted approach for robust clustering is presented in this work. The method is i...
It is well-known that outliers and noisy data can be very harmful when applying clustering methods....
It is well-known that outliers and noisy data can be very harmful when applying clustering methods....
The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and...
The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and...
We assess the performance of state-of-the-art robust clustering tools for regression structures unde...
Trimming principles play an important role in robust statistics. However, their use for clustering ...
A new method for performing robust clustering is proposed. The method is designed with the aim of fi...
TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-m...
Outliers can be extremely harmful when applying well-known Cluster Analysis methods. More- over, clu...
Outliers can be extremely harmful when applying well-known Cluster Analysis methods. More- over, clu...
Producción CientíficaTwo key questions in Clustering problems are how to determine the number of gr...
An iteratively reweighted approach for robust clustering is presented in this work. The method is i...
Outlying data can heavily influence standard clustering methods. At the same time, clustering princi...
An iteratively reweighted approach for robust clustering is presented in this work. The method is i...
An iteratively reweighted approach for robust clustering is presented in this work. The method is i...
It is well-known that outliers and noisy data can be very harmful when applying clustering methods....
It is well-known that outliers and noisy data can be very harmful when applying clustering methods....
The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and...
The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and...
We assess the performance of state-of-the-art robust clustering tools for regression structures unde...
Trimming principles play an important role in robust statistics. However, their use for clustering ...