Robust constrained fuzzy clustering
It is well-known that outliers and noisy data can be very harmful when applying clustering methods. Several fuzzy clustering methods which are able to handle the presence of noise have been proposed....
View ArticleA fast algorithm for robust constrained clustering
The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and Rousseeuw and van Driessen’s fast-MCD algorithm. Despite this coincidence, it is not completely...
View ArticleThe TCLUST Approach to Robust Cluster Analysis
A 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...
View ArticleAvoiding Spurious Local Maximizers in Mixture Modeling
The maximum likelihood estimation in the finite mixture of distributions setting is an ill-posed problem that is treatable, in practice, through the EM algorithm. However, the existence of spurious...
View ArticleExploring the number of groups in robust model-based clustering
Two key questions in Clustering problems are how to determine the number of groups properly and measure the strength of group-assignments. These questions are specially involved when the presence of...
View ArticleAnalysis of Fault Signatures for the Diagnosis of Induction Motors fed by...
Condition monitoring of induction motors fed by Voltage Source Inverters is challenging since the influence of the supply complicates the use of methods valid for utility fed motors. When trying to...
View ArticleDiscusión del articulo "Multivariate functional outlier detection”
Este trabajo es una discusión del articulo "Multivariate functional outlier detection” realizado por M. Hubert, P. Rousseeuw y P. Segaert.
View ArticleRobust Principal Component Analysis Based On Trimming Around Affine Subspaces
Principal Component Analysis (PCA) is a widely used technique for reducing dimensionality of multivariate data. The principal component subspace is defined as the affine subspace of a given dimension d...
View ArticleA Fuzzy Approach to Robust Clusterwise Regression
new robust fuzzy linear clustering method is proposed. We estimate coe cients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a xed proportion of...
View ArticleFinding the Number of Groups in Model-Based Clustering via Constrained...
Deciding the number of clusters k is one of the most difficult problems in Cluster Analysis. For this purpose, complexity-penalized likelihood approaches have been introduced in model-based clustering,...
View ArticleA Reweighting Approach to Robust Clustering
An iteratively reweighted approach for robust clustering is presented in this work. The method is initialized with a very robust clustering partition based on an high trimming level. The initial...
View ArticleInforme MANU
Este informe tiene dos objetivos, en primer lugar presentar evidencias a favor de la siguiente Tesis: La utilización de las Notas de Acceso (NA) a la Universidad como criterio para asignar las plazas...
View ArticleA bootstrap based measure robust to the choice of normalization methods for...
Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the...
View ArticleCluster analysis with cellwise trimming and applications to robust clustering...
In this work, we propose a robust Cluster Analysis methodology based on cell trimming as an extension to a recently introduced robust version of Principal Component Analysis. This new approach allows...
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