Large datasets for analysis

• [PDF File]Image segmentation evaluation for very-large datasets

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  Keywords: large-scale evaluation, large datasets, image segmentation 1. INTRODUCTION Fully automated analysis of medical images will provide a set of quantitative measurements that will be able to guide and improve physician decision-making. Medical images, especially those that are obtained periodically in the context of

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• [PDF File]Strategies and Algorithms for Clustering Large Datasets: A ...

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  4 Scalability strategies 6 Some of the strategies are also dependent on the type of data that is used. For instance, only clustering algorithms that incrementally build the partition can be used for data streams.

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• [PDF File]Analysis of Various I/O Methods for Large Datasets in C++

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  are analysed. The analysis is done on very large random text files generated by using a PRNG (pseudo random number generator). When dealing with huge datasets, time becomes an important constraint and so using the results of this analysis, a suitable input/output method can be selected

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• [PDF File]NTU RGB+D: A Large Scale Dataset for 3D Human Activity ...

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  activity analysis datasets and methods, readers are referred to these survey papers [47, 1, 5, 12, 21, 45, 3]. 2.1. 3D activity analysis datasets After the release of Microsoft Kinect [48], several datasets are collected by different groups to perform re-search on 3D action recognition and to evaluate different methods in this field.

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• SSECRETT and NeuroTrace: Interactive Visualization and ...

  Analysis Tools for Large-Scale Neuroscience Datasets Won-Ki Jeong, Johanna Beyer, Markus Hadwiger, Rusty Blue, Charles Law, Amelio Vazquez, Clay Reid, Jeff Lichtman, Hanspeter Pfister Abstract—Recent advances in optical and electron microscopy allow scientists to acquire extremely high-resolution images for neu-roscience research.

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• [PDF File]Large Datasets and You: A Field Guide

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  extremely large datasets. R (R Development Core Team, 2012), for example, works by holding objects in its virtual memory, and big datasets are often larger then the size of the RAM that is available to researchers using their operating software. Many of these problems

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• [PDF File]High Performance Multidimensional Analysis of Large Datasets

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  High Performance Multidimensional Analysis of Large Datasets * Sanjay Goil Alok Choudhary Center for Parallel and Distributed Computing, Department of Electrical & Computer Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Evanston, IL-60208 {sgoil,choudhsr}Qece.nwu.edu Abstract

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• [PDF File]Visualization Databases for the Analysis of Large Complex ...

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  subsets. Section 1 discussed the use of a large num-ber of plotted points per observation that is commonly carried out for small datasets. Achieving comprehen-sive analysis of a large dataset requires preserving this for the subsets, analyzing each in detail. The sam-pling frame can vary from one analysis method to the next.

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• Cluster Analysis for Large Datasets: An Effective ...

  Cluster Analysis for Large Datasets: An Effective Algorithm for Maximizing the Mixture Likelihood Daniel A. COLEMAN and David L. WOODRUFF The primary model for cluster analysis is the latent class model. This model yields the mixture likelihood. Due to numerous local maxima, the success of the EM algorithm

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• [PDF File]Analyzing and Interpreting Large Datasets Advanced Course

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  ANALYZING AND INTERPRETING LARGE DATASETS FACILITATOR/MENTOR GUIDE |4. 2. Classroom: There are two options for classroom training. For option a, participants read the training material . prior. to attending class and then review what they read in class. For option b, participants read the training material during class. a. Participants read ...

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• Secondary analysis of national survey datasets

  the analysis of large national survey datasets, the process will be challenging and the results may be unreliable. Researchers planning to analyze national survey datasets must recognize unique issues pertinent to survey data quality at the beginning so that the potential for introducing threats to reliability and

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• [PDF File]SAS Techniques for Managing Large Datasets

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  A 'large' dataset implies numerous observations and variables resulting in an increase in overall size, but is a subjective term that primarily depends on user perception and on the available resources and storage space. Large datasets often occur during integration or pooling of data from various studies.

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• Using case‐based approaches to analyse large datasets: a ...

  Using case-based approaches to analyse large datasets: a comparison of Ragin’s fsQCA and fuzzy cluster analysis Barry Cooper* and Judith Glaesser School of Education, Durham University, Durham, UK Taylor and FrancisTSRM_A_483079.sgm(Received 29 September 2009; final version received 31 March 2010)

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• [PDF File]Robust De-anonymization of Large Sparse Datasets

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  Datasets containing micro-data, that is, information about specific individuals, are increasingly becoming ... Our third contribution is a practical analysis of the Netflix Prize dataset, containing anonymized ... In a large database, for any except the rarest attributes, therearehundredsofrecordswith thesamevalueofthis

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• [PDF File]An Introduction to Secondary Data Analysis

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  • Analysis of secondary data, where “secondary data can include any ... • Thankfully, large secondary datasets generally have pre-constructed weights • However, multiple weights may exist for any one dataset –Appropriate selection and application of weights is the

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• [PDF File]Analyzing and Interpreting Large Datasets

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  Before attempting data analysis for large datasets, it is very important you locate the survey sampling methodology, questionnaire, data variable dictionary and any other supporting documentation. Activity . Activity #1: Go to the NHANES links below and describe what key information they

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• [PDF File]Symbolic data analysis approach to clustering large datasets

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  Symbolic clustering of large datasets 2 Introduction Lots of large datasets are available in databases. For the description of the data vector descriptions are usually used. Each its component corresponds to a variable which can be measured in different scales (nominal, ordinal, or numeric). Most of the well known clustering methods are implemented

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• [PDF File]Finding Linear Structure in Large Datasets with Scalable ...

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  Canonical Correlation Analysis (CCA) is a widely used spectral technique for finding cor-relation structures in multi-view datasets. In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requir-ing computing the product of two huge matri-ces and huge matrix decomposition, are compu-

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• [PDF File]Harmonization of large MRI datasets for the analysis of ...

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  Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan Raymond Pomponioa,*, Guray Erusa, Mohamad Habesa,b, Jimit Doshia, Dhivya Srinivasana, Elizabeth Mamouriana, Vishnu Bashyama, Ilya M. Nasrallaha,g, Theodore D. Satterthwaitel,

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