Results 251  300 of 1824  
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Haseena Hassan Hamsa   2011
The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy Cmean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed ...


Xue L   2010
We propose a new estimation method for multivariate failure time data using the quadratic inference function (QIF) approach. The proposed method efficiently incorporates withincluster correlations. Therefore, it is more efficient than those that ignore withincluster correlation. Furthermore, the proposed method is easy to implement. Unlike the weighted estimating equations in ...


Ehrl Lyonel   2009
The generation and geometrical analysis of clusters composed of rigid monodisperse primary particles with variable fractal dimension, df, in the range from 2.2 to 3 are presented. For all generated aggregate populations, it was found that the dimensionless aggregate mass, i, and the aggregate size, characterized by the radius of ...


White Laura Forsberg   2009
Methods to monitor spatial patterns of disease in populations are of interest in public health practice. The M statistic uses interpoint distances between cases to detect abnormalities in the spatial patterns of diseases. This statistic compares the observed distribution of interpoint distances with that which is expected when no unusual ...


MorinDuchesne Alexi   2009
A FortuinKasteleyn cluster on a torus is said to be of type {a,b},a,b in Z , if it is possible to draw a curve belonging to the cluster that winds a times around the first cycle of the torus as it winds b times around the second. Even though the ...


Huang Lan   2009
There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active ...


Freidlin Raisa Z   2009
The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed ...


Li Xin   2010
Partially observable Markov decision process (POMDP) is a commonly adopted mathematical framework for solving planning problems in stochastic environments. However, computing the optimal policy of POMDP for largescale problems is known to be intractable, where the high dimensionality of the underlying belief space is one of the major causes. In ...


Wolf Michael T   2009
This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results ...


Palencia Edwin R   2009
The Aspergillus niger aggregate within the A. section Nigri is a group of blackspored aspergilli of great agroeconomic importance whose well defined taxonomy has been elusive. RepPCR has become a rapid and costeffective method for genotyping fungi and bacteria. In the present study, we evaluated the discriminatory power of a ...


Sun Wei   2009
A stepwisecluster microbial biomass inference (SMI) model was developed through introducing stepwisecluster analysis (SCA) into composting process modeling to tackle the nonlinear relationships among state variables and microbial activities. The essence of SCA is to form a classification tree based on a series of cutting or mergence processes according to ...


Kim SungSuk   2010
In this study, we are concerned with a method for constructing quantumbased adaptive neurofuzzy networks (QANFNs) with a TakagiSugenoKang (TSK) fuzzy type based on the fuzzy granulation from a given inputoutput data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive ...


Castanheira Marlos   2010
The environment in which the horse is reared affects its ability to maintain thermal balance which is in turn related to thermal characteristics and regulatory physiological mechanisms. In this study a multivariate analysis of physiological traits in relation to heat tolerance in horses was carried out in the Federal District, ...


Varin Thibault   2009
Ward's method is extensively used for clustering chemical structures represented by 2D fingerprints. This paper compares Ward clusterings of 14 datasets (containing between 278 and 4332 molecules) with those obtained using the SzékelyRizzo clustering method, a generalization of Ward's method. The clusters resulting from these two methods were evaluated by ...


Shao Jia   2009
We propose a "nonconsensus" opinion model that allows for stable coexistence of two opinions by forming clusters of agents holding the same opinion. We study this nonconsensus model on lattices, several model complex networks, and a reallife social network. We find that the model displays a phase transition behavior characterized ...


Müller Christoph   2009
We present a development environment for distributed GPU computing targeted for multiGPU systems, as well as graphics clusters. Our system is based on CUDA and logically extends its parallel programming model for graphics processors to higher levels of parallelism, namely, the PCI bus and network interconnects. While the extended API ...


Torvik Vetle I   2009
BACKGROUND: We recently described "Authority," a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in ...


Marston Louise   2009
Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can ...


GarcíaGarcía Darío   2009
We review the existing alternatives for defining modelbased distances for clustering sequences and propose a new one based on the KullbackLeibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in realworld scenarios, a model selection scheme is also proposed.


Krivitsky Pavel N   2009
Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and nonbinary network data. ...


Objective selection of EEG late potentials through residual dependence estimation of independent ...
Milanesi M   2009
This paper presents a novel method to objectively select electroencephalographic (EEG) cortical sources estimated by independent component analysis (ICA) in eventrelated potential (ERP) studies. A proximity measure based on mutual information is employed to estimate residual dependences of the components that are then hierarchically clustered based on these residual dependences. ...


Cheung Y B   2009
OBJECTIVES: To compare three estimators of association between growth stunting as measured by heightforage Zscore and cognitive ability in children, and to examine the extent statistical adjustment for covariates is useful for removing confounding due to socioeconomic status. METHODS: Three estimators, namely randomeffects, within and betweencluster estimators, for panel data ...


Ersbøll A K   2009
The Kfunction is often used to detect spatial clustering in spatial point processes, e.g. clustering of infected herds. Clustering is identified by testing the observed Kfunction for complete spatial randomness modelled, e.g. by a homogeneous Poisson process. The approach provides information about spatial clustering as well as the scale of ...


Shah Sohrab P   2009
Analysis of array comparative genomic hybridization (aCGH) data for recurrent DNA copy number alterations from a cohort of patients can yield distinct sets of molecular signatures or profiles. This can be due to the presence of heterogeneous cancer subtypes within a supposedly homogeneous population. We propose a novel statistical method ...


Fan Liya L Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China.   2009
EMAN is one of the most popular software packages for single particle reconstruction. But the particle clusters produced during its model refining stage are of low qualities. We attempt to refine the particle clusters by more accurately determining orientations of particles, and thereby achieving higher resolutions of consequent 3D structures. ...


Yuan Ping P National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan Province,   2009
To investigate the spatial distribution patterns of anorectal atresia/stenosis in China. Data were collected from the Chinese Birth Defects Monitoring Network (CBDMN), a hospitalbased congenital malformations registry system. All fetuses more than 28 wk of gestation and neonates up to 7 d of age in hospitals within the monitoring sites ...


Nanetti Luca   2009
Kmeans clustering has become a popular tool for connectivitybased cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. ...


Wang Kaijun   2009
The estimation of the number of clusters (NC) is one of crucial problems in the cluster analysis of gene expression data. Most approaches available give their answers without the intuitive information about separable degrees between clusters. However, this information is useful for understanding cluster structures. To provide this information, we ...


Erez Keren   2009
In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated ...


Perez Andres M   2009
Considerable attention has been given lately to the need for global systems for animal disease surveillance that support realtime assessment of changing temporalspatial risks. Until recently, however, prospects for development of such systems have been limited by the lack of informatics tools and an overarching collaboration framework to enable realtime ...


MartínezLópez B   2009
Social network analysis was used in combination with techniques for detection of temporalspatial clusters to identify operations at high risk of receiving or dispatching pigs, from January through December 2005, in the Spanish province of Salamanca. The temporalspatial structure of the network was explicitly analyzed to estimate the statistical significance ...


Zhu Shanfeng   2009
MOTIVATION: Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the innerproduct of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into ...


Li Xiang   2009
Fuzzy cmeans (FCM) clustering is an unsupervised method derived from fuzzy logic that is suitable for solving multiclass and ambiguous clustering problems. In this study, FCM clustering is applied to cluster metabolomics data. FCM is performed directly on the data matrix to generate a membership matrix which represents the degree ...


Schmidt Kristi E   2009
The primary objectives of this research are to identify the underlying clusters of design variables affecting the perceived usability of a webpage and to examine the effects of webpage design variables on webpage performance. Fiftyseven design variables and 10 underlying clusters that conceptualise the structure of user webpage judgement are ...


Geraci Filippo   2009
Microarray technology for profiling gene expression levels is a popular tool in modern biological research. Applications range from tissue classification to the detection of metabolic networks, from drug discovery to timecritical personalized medicine. Given the increase in size and complexity of the data sets produced, their analysis is becoming problematic ...


Givoni Inmar E   2009
Affinity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplarbased clustering. We present a derivation of AP that is much simpler than the original one and is based on a quite different graphical model. The new model allows easy derivations of message updates for extensions and modifications ...


Huang Xianzheng   2009
Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihoodbased inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for randomeffect model misspecification in GLMMs for clustered binary response. ...


Thomson Andrew A Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, U.K.   2009
Cluster randomized trials (CRTs) are increasingly used to evaluate the effectiveness of healthcare interventions. A key feature of CRTs is that the observations on individuals within clusters are correlated as a result of betweencluster variability. Sample size formulae exist which account for such correlations, but they make different assumptions regarding ...


Tzortzis Grigorios F   2009
Kernel kmeans is an extension of the standard k means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel kmeans algorithm, a deterministic and incremental approach to kernelbased clustering. Our method adds one cluster at ...


Rennie T W   2009
The aim of this study was to demonstrate the epidemiological use of multiple correspondence analysis (MCA), as applied to tuberculosis (TB) data from North East London. Data for TB notifications in North East London primary care trusts (PCTs) between the years 2002 and 2007 were used. TB notification data were ...


Pavlopoulos Georgios A   2009
jClust is a userfriendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are kMeans, Affinity propagation, ...


Siegbahn Per E M   2009
The quantum chemical cluster approach for modeling enzyme reactions is reviewed. Recent applications have used cluster models much larger than before which have given new modeling insights. One important and rather surprising feature is the fast convergence with cluster size of the energetics of the reactions. Even for reactions with ...


Chatterjee Priyam   2009
In this paper, we propose KLLD: a patchbased, locally adaptive denoising method based on clustering the given noisy image into regions of similar geometric structure. In order to effectively perform such clustering, we employ as features the local weight functions derived from our earlier work on steering kernel regression . ...


Lin Lanjia L Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA.   2010
We consider analysis of clustered data with mixed bivariate responses, i.e., where each member of the cluster has a binary and a continuous outcome. We propose a new bivariate random effects model that induces associations among the binary outcomes within a cluster, among the continuous outcomes within a cluster, between ...


Hore Prodip   2009
An ensemble of clustering solutions or partitions may be generated for a number of reasons. If the data set is very large, clustering may be done on tractable size disjoint subsets. The data may be distributed at different sites for which a distributed clustering solution with a final merging of ...


Stone Eric A   2009
In recent years, the advent of highthroughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Wholegenome transcriptional profiling is a standard component of systemslevel analyses, ...


Grassberger Peter   2009
We simulate directed site percolation on two lattices with four spatial and one timelike dimensions (simple and bodycentered hypercubic in space) with the standard single cluster spreading scheme. For efficiency, the code uses the same ingredients (hashing, histogram reweighing, and improved estimators) as described by Grassberger [Phys. Rev. E 67, ...


Wilkinson Tim J   2009
PURPOSE: Assessing professionalism is hampered by varying definitions and these definitions' lack of a clear breakdown of the elements of professionalism into aspects that can be measured. Professionalism is multidimensional, so a combination of assessment tools is required. In this study, conducted during 20072008, the authors aimed to match assessment ...


Feldt S S Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.   2009
We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive ...


Schuurman Nadine   2009
The aim of this study was to examine spatial clustering of obesity and/or moderate physical activity and their relationship to a neighborhood's built environment. Data on levels of obesity and moderate physical activity were derived from the results of a telephone survey conducted in 2006, with 1,863 survey respondents in ...


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