AIMC Topic: Cluster Analysis

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Design of double fuzzy clustering-driven context neural networks.

Neural networks : the official journal of the International Neural Network Society
In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-...

HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised classification of superfamily protein sequences with a reliable cut-off threshold.

PloS one
BACKGROUND: Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear s...

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

Bioelectromagnetics
For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed...

Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Over the last decade, the application of computer vision techniques to the analysis of behavioural patterns has seen a considerable increase in research interest. One such interesting and recent application is the visual be...

Multilayer bootstrap networks.

Neural networks : the official journal of the International Neural Network Society
Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-cent...

Bayesian estimation of multidimensional latent variables and its asymptotic accuracy.

Neural networks : the official journal of the International Neural Network Society
Hierarchical learning models, such as mixture models and Bayesian networks, are widely employed for unsupervised learning tasks, such as clustering analysis. They consist of observable and latent variables, which represent the given data and their un...

Molecular identification and in vitro antifungal susceptibility of Scedosporium complex isolates from high-human-activity sites in Mexico.

Mycologia
The genus Scedosporium is a complex of ubiquitous moulds associated with a wide spectrum of clinical entities, with high mortality principally in immunocompromised hosts. Ecology of these microorganisms has been studied performing isolations from env...

Possible world based consistency learning model for clustering and classifying uncertain data.

Neural networks : the official journal of the International Neural Network Society
Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possib...

Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.

Journal of neuroscience methods
BACKGROUND: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algor...