Publicação

Sliding PCA fuzzy clustering algorithm

Detalhes bibliográficos
Resumo:	This paper proposes a new robust approach to nonlinear clustering based on the Principal Component Analysis (PCA) approach. A robust c-means partition is derived by using the natural PCA noise-rejection mechanism and the nonlinearity captured by a sliding process of the clusters prototype. A non-linear extension of PCA has been developed for detecting the lower-dimensional representation of real world data sets. For these cases local linear approaches are used widely because of their computational simplicity and understandability. We will present a new method that joins (merges) the fuzzy clustering algorithm with a local sliding PCA analysis. With this strategy it is possible to identify the non-linear relations and obtain morphological information of the data. The Sliding PCA-Fuzzy cluster algorithm (SPCA-FCA) is a fuzzy clustering algorithm that estimates local principal component vectors as the vectors spanning prototypes of clusters, performed on the neighborhood of the center of cluster and normal approximations in order to estimate a tangent surface that characterizes the trend and curvature of the data points or contours region. Numerical experiments demonstrate that the proposed method is useful for capturing cluster cores by rejecting noise samples, and we can easily assess cluster validity by using cluster-crossing curves.
Autores principais:	Salgado, Paulo
Outros Autores:	Gonçalves, Lio; Igrejas, Getúlio
Assunto:	Principal component Analysis Fuzzy clustering
Ano:	2011
País:	Portugal
Tipo de documento:	comunicação em conferência
Tipo de acesso:	acesso restrito
Instituição associada:	Instituto Politécnico de Bragança
Idioma:	inglês
Origem:	Biblioteca Digital do IPB

Descrição
Resumo:	This paper proposes a new robust approach to nonlinear clustering based on the Principal Component Analysis (PCA) approach. A robust c-means partition is derived by using the natural PCA noise-rejection mechanism and the nonlinearity captured by a sliding process of the clusters prototype. A non-linear extension of PCA has been developed for detecting the lower-dimensional representation of real world data sets. For these cases local linear approaches are used widely because of their computational simplicity and understandability. We will present a new method that joins (merges) the fuzzy clustering algorithm with a local sliding PCA analysis. With this strategy it is possible to identify the non-linear relations and obtain morphological information of the data. The Sliding PCA-Fuzzy cluster algorithm (SPCA-FCA) is a fuzzy clustering algorithm that estimates local principal component vectors as the vectors spanning prototypes of clusters, performed on the neighborhood of the center of cluster and normal approximations in order to estimate a tangent surface that characterizes the trend and curvature of the data points or contours region. Numerical experiments demonstrate that the proposed method is useful for capturing cluster cores by rejecting noise samples, and we can easily assess cluster validity by using cluster-crossing curves.