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Fuzzy C-Means (FCM) is the most popular algorithm of the fuzzy clustering approach. Although FCM and its variations have shown good performance in cluster detection, they do not consider that different variables could produce different membership degrees. Motivated by this, the Multi-variate Fuzzy C-Means (MFCM) method was proposed. The MFCM computes membership degrees of both clusters and variables...
The main property of kernel methods is that they can implicitly perform a nonlinear mapping of the input data into a high-dimensional space. This mapping allows to find a simpler structure within space without increasing the number of parameters increasing the clustering quality. Therefore, kernel methods may find better results for data arranged not linearly. Many methods presented in the literature...
This article presents a new method for shape description suitable to be used as a solution to the retrieval problem in large image collections. The proposed approach, called Multiscale Symbolic Data Descriptor (MSDD) combines multiscale methods with Symbolic Data Analysis. The contour convexities and concavities at different scale levels are represented using a two-dimensional matrix from which we...
This paper proposes a nonparametric multiple regression method for interval data. Regression smoothing investigates the association between an explanatory variable and a response variable. Here, each interval variable of the input data is represented by its range and center and a smooth function between a pair of vector of interval variables is defined. In order to test the suitability of the proposed...
Clustering analysis is an important tool used in several application domains like pattern recognition, computer vision and computational biology to summarize data. The fuzzy c-means method (FCM) is the most popular fuzzy clustering algorithm, however this method is sensitive to noisy data. The possibilistic c-means (PCM) was created as an alternative to solve this problem. The propose in this work...
Kernel clustering methods have been very important in application of non-supervised machine learning to real problems. Kernel methods possess many advantages other than non-linearity such as modularity, ability to work with heterogeneous descriptions of data, incorporation of prior knowledge etc. In this paper, we present a clustering method based on kernel functions for partitioning a set of interval-valued...
Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been compared our method with a dynamic clustering...
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