Search results for "cluster analysis."
showing 10 items of 805 documents
Clustering-Assisted 3D Beamforming for Throughput Maximization in mmWave Networks
2021
Beamforming schemes have been widely used to improve network throughput in 5G mmWave networks. However, 3D beamforming schemes have hereto not been investigated in this context. In this work, a cluster-assisted 3D beamforming scheme is proposed to optimize the downtilt angle for network coverage and throughput maximization. User Equipment (UEs) are clustered based on inter-user and the inter-cluster distances. The interference is accounted from the adjacent clusters and thus frequency resources can be assigned to the non-adjacent clusters. Optimal downtilt angles are obtained for every cluster to maximize the throughput while considering the interference from adjacent clusters. 3D beam patt…
Cluster-based active learning for compact image classification
2010
In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer…
Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors.
2013
Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single…
A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
2012
Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonove…
Association between WHO cut-offs for childhood overweight and obesity and cardiometabolic risk
2012
AbstractObjectiveTo examine the association between cardiovascular risk and childhood overweight and obesity using the BMI cut-offs recommended by the WHO.DesignChildren were classified as normal weight, overweight and obese according to the WHO BMI-for-age reference. Blood pressure, lipids, glucose, insulin, homeostasis model assessment–insulin resistance (HOMA-IR) and uric acid levels were compared across BMI groups. ANOVA and tests of linearity were used to assess overall mean differences across groups. Crude and adjusted odds ratios were calculated for adverse plasma levels of biochemical variables.SettingPaediatric care centres.SubjectsChildren (n149) aged 8–18 years.ResultsAbout 37 %,…
Phenotyping of type 2 diabetes mellitus at onset on the basis of fasting incretin tone: Results of a two-step cluster analysis.
2015
Aims/Introduction According to some authors, in type 2 diabetes there is a reduced postprandial action of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). However, little is known about the role of fasting incretins in glucose homeostasis. Our aim was to evaluate, through a two-step cluster analysis, the possibility of phenotyping patients with type 2 diabetes at onset on the basis of fasting GLP-1, GIP and ghrelin. Materials and Methods A total of 96 patients with type 2 diabetes within 6 months of onset (mean age 62.40 ± 6.36 years) were cross-sectionally studied. Clinical, anthropometric and metabolic parameters were evaluated. At fasting the follow…
Processing of rock core microtomography images: Using seven different machine learning algorithms
2016
The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…
Improving clustering of Web bot and human sessions by applying Principal Component Analysis
2019
View references (18) The paper addresses the problem of modeling Web sessions of bots and legitimate users (humans) as feature vectors for their use at the input of classification models. So far many different features to discriminate bots’ and humans’ navigational patterns have been considered in session models but very few studies were devoted to feature selection and dimensionality reduction in the context of bot detection. We propose applying Principal Component Analysis (PCA) to develop improved session models based on predictor variables being efficient discriminants of Web bots. The proposed models are used in session clustering, whose performance is evaluated in terms of the purity …
Class discovery from semi-structured EEG data for affective computing and personalisation
2017
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not exp…
Fast dendrogram-based OTU clustering using sequence embedding
2014
Biodiversity assessment is an important step in a metagenomic processing pipeline. The biodiversity of a microbial metagenome is often estimated by grouping its 16S rRNA reads into operational taxonomic units or OTUs. These metagenomic datasets are typically large and hence require effective yet accurate computational methods for processing.In this paper, we introduce a new hierarchical clustering method called CRiSPy-Embed which aims to produce high-quality clustering results at a low computational cost. We tackle two computational issues of the current OTU hierarchical clustering approach: (1) the compute-intensive sequence alignment operation for building the distance matrix and (2) the …