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Musa Ibrahim Musa Ishag - - 2010
Data mining applications over on-body sensor data have earned great attention in recent years. We propose a novel Online Multi-divisive Hierarchical Clustering Method on on-body sensor data. Our method evolves tree-like top down hierarchy cluster, which splits and agglomerates clusters as needed. Experimental results prove a competing quality for our ...
Carugo Oliviero - - 2010
Cluster analysis is an unsupervised pattern recognition frequently used in biology, where large amounts of data must often be classified. Hierarchical agglomerative approaches, the most commonly used techniques in biology, are described in this chapter. Particular attention is put on techniques for validating the optimal cluster number and the clustering ...
Sesli M - - 2010
Five different similarity coefficients (Jaccard, Sorensen-Dice, simple matching, Rogers and Tanimoto, and Russel and Rao) were evaluated and 10 wild olives analyzed with RAPD markers. The influence of the similarity coefficients on wild olives clustering was investigated. Forty-five primers were used on samples from 10 wild olives (Wild 1 and ...
Guo Ying - - 2010
Brain imaging data have shown great promise as a useful predictor for psychiatric conditions, cognitive functions and many other neural-related outcomes. Development of prediction models based on imaging data is challenging due to the high dimensionality of the data, noisy measurements, complex correlation structures among voxels, small sample sizes, and ...
Gómez-Rubio Virgilio - - 2010
In this chapter we provide a summary of different methods for the detection of disease clusters. First of all, we give a summary of methods for computing estimates of the relative risk. These estimates provide smoothed values of the relative risks that can account for its spatial variation. Some methods ...
Gangnon Ronald E - - 2010
The spatial scan statistic is a widely applied tool for cluster detection. The spatial scan statistic evaluates the significance of a series of potential circular clusters using Monte Carlo simulation to account for the multiplicity of comparisons. In most settings, the extent of the multiplicity problem varies across the study ...
Lutambi Angelina M - - 2010
Childhood mortality remains an important subject, particularly in sub-Saharan Africa where levels are still unacceptably high. To achieve the set Millennium Development Goals 4, calls for comprehensive application of the proven cost-effective interventions. Understanding spatial clustering of childhood mortality can provide a guide in targeting the interventions in a more ...
Kraus Johann M - - 2010
In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches ...
Banerjee Amit Kumar - - 2010
Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater ...
Zheng Fengbin - - 2010
A subset selected by a supervised feature selection method may not be a good one for unsupervised learning and vice versa. We propose a novel Feature Selection algorithm through Feature Clustering, FSFC. FSFC does not need the class label information in the data set and is suitable for both supervised ...
Silcocks Paul - - 2010
Typical advice on the design and analysis of cluster randomized trials (C-RCTs) focuses on allowance for the clustering at the level of the unit of allocation. However often C-RCTs are also organised spatially as may occur in the fields of Public Health and Primary Care where populations may even overlap. ...
Mäs Michael - - 2010
One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of ...
Ozyigit Tamer - - 2010
The term decompression illness (DCI) describes maladies resulting from inadequate decompression, but there is little consensus concerning clinically useful DCI subclasses. Our aim was to explore an objective DCI classification using multivariate statistics to assess naturally associated clusters of DCI manifestations. We also evaluated their mapping onto other DCI classifications ...
Brunenberg Ellen - - 2010
Dissimilarity measures for DTI clustering are abundant. However, for HARDI, the L2 norm has up to now been one of only few practically feasible measures. In this paper we propose a new measure, that not only compares the amplitude of diffusion profiles, but also rewards coincidence of the extrema. We ...
von Eye A - - 2010
Standard cluster analysis creates clusters based on the criterion that their members be closer to each other than to members of other clusters. In this article, it is proposed to examine empirical clusters that result from standard clustering, with the goal of assessing whether they contradict distributional assumptions. Four models ...
White James R JR Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, - - 2010
Molecular studies of microbial diversity have provided many insights into the bacterial communities inhabiting the human body and the environment. A common first step in such studies is a survey of conserved marker genes (primarily 16S rRNA) to characterize the taxonomic composition and diversity of these communities. To date, however, ...
Sugár István P - - 2010
There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions ...
Lock Eric F EF Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, - - 2010
Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without ...
Hartsperger Mara L - - 2010
Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k ...
Georgi Benjamin - - 2010
Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in ...
Nugent Rebecca - - 2010
In molecular biology, we are often interested in determining the group structure in, e.g., a population of cells or microarray gene expression data. Clustering methods identify groups of similar observations, but the results can depend on the chosen method's assumptions and starting parameter values. In this chapter, we give a ...
Van Meter Emily M - - 2010
This paper addresses the statistical use of accessibility and availability indices and the effect of study boundaries on these measures. The measures are evaluated via an extensive simulation based on cluster models for local outlet density. We define outlet to mean either food retail store (convenience store, supermarket, gas station) ...
Oyana Tonny J - - 2010
The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique-the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means ...
Martins David - - 2010
Background. Renal disease is commonly described as a complication of metabolic syndrome (MetS) but some recent studies suggest that Chronic Kidney disease (CKD) may actually antecede MetS. Few studies have explored the predictive utility of co-clustering CKD with MetS for cardiovascular disease (CVD) mortality. Methods. Data from a nationally representative ...
Mirghani Samia E - - 2010
Malaria infection and disease exhibit microgeographic heterogeneity which if predictable could have implications for designing small-area intervention. Here, the space-time clustering of Plasmodium falciparum infections using data from repeat cross-sectional surveys in Gezira State, a low transmission area in northern Sudan, is investigated. Data from cross-sectional surveys undertaken in January ...
Carlis John J Computer Science and Engineering Department, University of Minnesota, Minneapolis, MN 55455, USA. - - 2010
Clustering can be a valuable tool for analyzing large amounts of data, but anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter when working within each of the three available frameworks where one thinks of clustering: as ...
Prosperi Mattia C F - - 2010
Phylogenetic methods produce hierarchies of molecular species, inferring knowledge about taxonomy and evolution. However, there is not yet a consensus methodology that provides a crisp partition of taxa, desirable when considering the problem of intra/inter-patient quasispecies classification or infection transmission event identification. We introduce the threshold bootstrap clustering (TBC), a ...
Savory David J DJ Planned Systems International, Inc, Falls Church, VA 22041, - - 2010
The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF) worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses ...
Rapaport Franck - - 2010
Cancer progression is often driven by an accumulation of genetic changes but also accompanied by increasing genomic instability. These processes lead to a complicated landscape of copy number alterations (CNAs) within individual tumors and great diversity across tumor samples. High resolution array-based comparative genomic hybridization (aCGH) is being used to ...
Kustra Rafal - - 2010
While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on ...
Tomé A M AM IEETA/DETI, Universidade de Aveiro, 3810-193, Portugal. - - 2010
This work proposes a clustering technique to analyze evoked potential signals. The proposed method uses an orthogonal subspace model to enhance the single-trial signals of a session and simultaneously a subspace measure to group the trials into clusters. The ensemble averages of the signals of the different clusters are compared ...
Braun Elke - - 2010
Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural ...
Carugo Oliviero - - 2010
The present chapter provides the basic information about the measures of proximity between two subjects or groups of subjects. It is obvious that these concepts must be clear in order to apply them to any pattern recognition analysis, both supervised and unsupervised.
Jackson Monica C - - 2010
BACKGROUND: Investigation of global clustering patterns across regions is very important in spatial data analysis. Moran's I is a widely used spatial statistic for detecting global spatial patterns such as an east-west trend or an unusually large cluster. Here, we intend to improve Moran's I for evaluating global clustering patterns ...
Aldecoa Rodrigo - - 2010
How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount ...
Mahata Pritha - - 2010
Finding subtypes of heterogeneous diseases is the biggest challenge in the area of biology. Often, clustering is used to provide a hypothesis for the subtypes of a heterogeneous disease. However, there are usually discrepancies between the clusterings produced by different algorithms. This work introduces a simple method which provides the ...
Ho Candy P S CP Wolfson Medical Vision Lab, Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, UK. - - 2010
The detection of microcalcifications, reconstruction of clusters of microcalcifications and their subsequent classification into malignant and benign are important tasks in the early detection of breast cancer. Digital breast tomosynthesis (DBT) provides new opportunities in such tasks. By utilizing the multiple projections in DBT and using the geometry of DBT, ...
Petersen Frank - - 2010
Based on increasing knowledge on the pathogenesis of rheumatoid arthritis (RA), more and more potential therapeutics have been developed. To evaluate their therapeutic efficacy, safety and toxicity, appropriate animal models are required. Although rodent models of RA have been extensively used for preclinical evaluation, the differences between rodents and humans ...
Tindall Elizabeth A - - 2010
High-throughput custom designed genotyping arrays are a valuable resource for biologically focused research studies and increasingly for validation of variation predicted by next-generation sequencing (NGS) technologies. We investigate the Illumina GoldenGate chemistry using custom designed VeraCode and sentrix array matrix (SAM) assays for each of these applications, respectively. We highlight ...
Rappert Brian - - 2010
Attempts to place limits on the conduct of conflict raise many practical and political concerns. This article asks how debates regarding precautionary approaches to risk might inform discussions about how limits are set for armed conflict. The 2008 Convention on Cluster Munitions (CCM) provides the starting point for this analysis. ...
Zare Habil - - 2010
Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address ...
Rodríguez-Sotelo J L JL Faculty of Electrical and Electronic Engineering, Universidad Nacional de Colombia sede Manizales, Colombia. - - 2010
A method that improves the feature selection stage for non-supervised analysis of Holter ECG signals is presented. The method corresponds to WPCA approach developed mainly in two stages. First, the weighting of the feature set through a weight vector based on M-inner product as distance measure and a quadratic optimization ...
Leblond Jeffrey D - - 2010
This study examined the sterol compositions of 102 dinoflagellates using clustering and cluster validation techniques, as a means of determining the relatedness of the organisms. In addition, dinoflagellate sterol-based relationships were compared statistically to 18S rDNA-based phylogenetic relationships using the Mantel test. Our results indicated that the examined dinoflagellates formed ...
Roca Pauline - - 2010
This paper presents a connectivity-based parcellation of the human post-central gyrus, at the level of the group of subjects. The dimension of the clustering problem is reduced using a set of cortical regions of interest determined at the inter-subject level using a surface-based coordinate system, and representing the regions with ...
Li Shuai Cheng - - 2010
Ab initio protein structure prediction methods generate numerous structural candidates, which are referred to as decoys. The decoy with the most number of neighbors of up to a threshold distance is typically identified as the most representative decoy. However, the clustering of decoys needed for this criterion involves computations with ...
Rajaram Satwik - - 2010
The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not ...
Guevara Pamela - - 2010
This paper presents a method inferring a model of the brain white matter organisation from HARDI tractography results computed for a group of subjects. This model is made up of a set of generic fiber bundles that can be detected in most of the population. Our approach is based on ...
Cançado André L F - - 2010
Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based ...
Landis Florian - - 2010
We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We ...
Austin Peter C - - 2010
Repeated cross-sectional cluster randomization trials are cluster randomization trials in which the response variable is measured on a sample of subjects from each cluster at baseline and on a different sample of subjects from each cluster at follow-up. One can estimate the effect of the intervention on the follow-up response ...
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