Navegando por Palavras-chave "clustering"

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    An Alternative Approach for Binary and Categorical Self-Organizing Maps
    (IEEE, 2017) Santana, Alessandra [UNIFESP]; Morais, Alessandra [UNIFESP]; Quiles, Marcos G. [UNIFESP]
    One of the most used neural network model for clustering data is the Self-Organizing Map (SOM). Over the years, it has been applied in many areas, from computing to biology, and therefore a wide range of data types have been considered. Originally, the SOM was developed to take real-valued data into account. Thus, learning other data types, such as binary and category data, remains a challenge. This work proposes an alternative and effective modified SOM, to better cluster binary and categorical data.
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    AUTOMATIC SUBCORTICAL TISSUE SEGMENTATION OF MR IMAGES USING OPTIMUM-PATH FOREST CLUSTERING
    (Ieee, 2011-01-01) Cappabianco, Fabio Augusto Menocci [UNIFESP]; Ide, Jaime Shinsuke [UNIFESP]; Falcao, Alexandre; Li, Chiang-shan R.; Universidade Federal de São Paulo (UNIFESP)
    Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.
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    Heuristics for minimizing the maximum within-clusters distance
    (Sociedade Brasileira de Pesquisa Operacional, 2012-12-01) Fioruci, José Augusto; Toledo, Franklina M.b.; Nascimento, Mariá Cristina Vasconcelos [UNIFESP]; Universidade de São Paulo (USP); Universidade Federal de São Paulo (UNIFESP)
    The clustering problem consists in finding patterns in a data set in order to divide it into clusters with high within-cluster similarity. This paper presents the study of a problem, here called MMD problem, which aims at finding a clustering with a predefined number of clusters that minimizes the largest within-cluster distance (diameter) among all clusters. There are two main objectives in this paper: to propose heuristics for the MMD and to evaluate the suitability of the best proposed heuristic results according to the real classification of some data sets. Regarding the first objective, the results obtained in the experiments indicate a good performance of the best proposed heuristic that outperformed the Complete Linkage algorithm (the most used method from the literature for this problem). Nevertheless, regarding the suitability of the results according to the real classification of the data sets, the proposed heuristic achieved better quality results than C-Means algorithm, but worse than Complete Linkage.
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    A Hybrid Heuristic for the k-medoids Clustering Problem
    (Assoc Computing Machinery, 2012-01-01) Nascimento, Maria C. V. [UNIFESP]; Toledo, Franklina M. B.; Carvalho, Andre C. P. L. F. de; Soule, T.; Universidade Federal de São Paulo (UNIFESP)
    Clustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem. Current versions of k-medoids rely on heuristics, with good results reported in the literature. However, few methods that analyze the quality of the partitions found by the heuristics have been proposed. in this paper, we propose a hybrid Lagrangian heuristic for the k-medoids. We compare the performance of the proposed Lagrangian heuristic with other heuristics for the k-medoids problem found in literature. Experimental results presented that the proposed Lagrangian heuristic outperformed the other algorithms.
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    Verbal fluency facilitated by the cholinergic blocker, scopolamine
    (Wiley-Blackwell, 2002-01-01) Pompeia, S.; Rusted, J. M.; Curran, H. V.; Univ Sussex; Universidade Federal de São Paulo (UNIFESP); UCL
    This study was designed to explore putative facilitatory effects of low doses of scopolamine (SP) on phonemic (letter) and semantic (category) verbal fluency. A double-blind, parallel-group design was used with 36 subjects who completed a test battery before and 2 h after 0.6 mg or 1.2 mg of SP or placebo. Fluency measures included total number of words generated, clustering (the production of words within semantic or phonemic subcategories) and switching (the ability to shift efficiently to new subcategories). Low doses of scopolamine increased phonemic fluency, as has been shown previously. Semantic fluency was not increased by SP, although subjects treated with 1.2 mg of SP generated higher-frequency words. SP did not affect clustering or switching. It is suggested that phonemic and semantic fluency reflect distinct cognitive processes. Copyright (C) 2002 John Wiley Sons, Ltd.