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检索条件"任意字段=Conference on Visualization and Data Analysis 2011"

共 1225 条记录，以下是441-450 订阅

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Hierarchical Disaster Image Classification for Situation Report Enhancement

Hierarchical Disaster Image Classification for Situation Rep...

引用

12th IEEE International conference on Information Reuse and Integration (IRI)/1st International Workshop on Issues and Challenges in Social Computing (WICSOC)

作者： Yang, Yimin Ha, Hsin-Yu Fleites, Fausto Chen, Shu-Ching Luis, Steven Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9781457709661

In this paper, a hierarchical disaster image classification (HDIC) framework based on multi-source data fusion (MSDF) and multiple correspondence analysis (MCA) is proposed to aid emergency managers in disaster response situations. The HDIC framework classifies images into different disaster categories and sub-categories using a pre-defined semantic hierarchy. In order to effectively fuse different sources (visual and text) of information, a weighting scheme is presented to assign different weights to each data resource depending on the hierarchical structure. The experimental analysis demonstrates that the proposed approach can effectively classify disaster images at each logical layer. In addition, the paper also presents an iPad application developed for situation report management using the proposed HDIC framework.

关键词： data models Feature extraction Mathematical model Multimedia communication Performance evaluation Training visualization

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Learning A Mixture of Sparse Distance Metrics for Classification and Dimensionality Reduction

Learning A Mixture of Sparse Distance Metrics for Classifica...

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IEEE International conference on Computer Vision (ICCV)

作者： Hong, Yi Li, Quannan Jiang, Jiayan Tu, Zhuowen Univ Calif Los Angeles Lab Neuro Imaging Los Angeles CA 90024 USA

ISBN: (纸本)9781457711022

This paper extends the neighborhood components analysis method (NCA) to learning a mixture of sparse distance metrics for classification and dimensionality reduction. We emphasize two important properties in the recent learning literature, locality and sparsity, and (1) pursue a set of local distance metrics by maximizing a conditional likelihood of observed data;and (2) add l(1)-norm of eigen values of the distance metric to favor low rank matrices of fewer parameters. Experimental results on standard UCI machine learning datasets, face recognition datasets, and image categorization datasets demonstrate the feasibility of our approach for both distance metric learning and dimensionality reduction.

关键词： Accuracy data visualization Eigenvalues and eigenfunctions Machine learning Measurement Support vector machines Training data

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A Classification of Cluster Validity Indexes Based on Membership Degree and Applications

A Classification of Cluster Validity Indexes Based on Member...

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International conference on Web Information Systems and Mining (WISM 2011)

作者： Xie, Nannan Hu, Liang Luktarhan, Nurbol Zhao, Kuo Jilin Univ Software Coll Changchun 130023 Peoples R China Comp Sci &Technol Coll Changchun Peoples R China Xinjiang Univ Informat Sci & Engn coll Urumqi Peoples R China

ISBN: (纸本)9783642239700

With the widely used of data mining and cluster analysis, cluster validation is attracting increasing attention. In this paper, the concept and development of cluster validation are introduced, then, based on the membership degree, a classification of cluster validity indexes is proposed: cluster validity indexes fit for crisp cluster, cluster validity indexes fit for fuzzy cluster. Based on this, combining with Cluster Validity analysis Platform (CVAP), describing the two most important usages of cluster validation: to find the optimal number of clusters and to find appropriate clustering algorithms to a particular data set. Experiments give visualization representation of clustering validation process.

关键词： Cluster validation Cluster validity index Cluster visualization

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Daily solar radiation prediction based on wavelet analysis

Daily solar radiation prediction based on wavelet analysis

引用

50th Annual conference on Society of Instrument and Control Engineers, SICE 2011

作者： Zhang, Peng Takano, Hirotaka Murata, Junichi Department of Electrical and Electronic Engineering Kyushu University Fukuoka Japan Department of Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (纸本)9784907764395

Solar radiation is an important factor in forecasting outputs of photovoltaic power systems. A method for solar radiation prediction is proposed based on wavelet transform. The data, including solar radiation and potential input variables, are decomposed into several time-frequency areas via wavelet transform. Given that some variables are relevant in some areas of solar radiation but irrelevant in other areas, we extract input variables from candidates separately in each time-frequency area. We perform principal component analysis (PCA) to extract principal components from input variables and we find their relations to the solar radiation are linear by visualization. Thus, linear regression equation is built to each area respectively, and the final result is the summation of predicted solar radiation from each time-frequency area. A comparison with the method using the same input variables in all time-frequency areas is presented and the results of our proposed method are more accurate. © 2011 SICE.

关键词： visualization

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Visualizing user activity in open e-learning contexts: Challenges and techniques for operational management 11

Visualizing user activity in open e-learning contexts: Chall...

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39th Annual ACM SIGUCCS conference, SIGUCCS'11

作者： McGrath, Owen G. Educational Technology Services University of California Berkeley United States

ISBN: (纸本)9781450310239

This paper describes the practical application of four visualization techniques that have been developed to deal with high-volume, large time-scaled, and high-dimensional data sets that are characteristic of Internet-based user activity. visualization techniques can be useful for monitoring and studying online user activity in settings where many thousands of users are involved in web-based educational endeavors. Simple numerical summaries of server requests and server performance trends cannot adequately answer the kinds of questions posed by those who desire to understand e-learner activity in web-based e-learning systems. Tracking and understanding remote users and their distant, round-the-clock activities present major technical and analytical challenges, especially in terms of the sheer scale and volume of the generated usage data. With web-based teaching and learning systems, four aspects of the usage tend to hinder analysis: high density, broad time scales, many variables, and veiled patterns. The paper looks at possible solutions in the application of four visualization techniques, using open source statistics software, in the contexts of key scenarios where such techniques can support operations management and decision making processes. © 2011 ACM.

关键词： data mining

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Using YouTube data to analyze human continent-level mobility

Using YouTube data to analyze human continent-level mobility

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2011 International conference on Computational Aspects of Social Networks, CASoN'11

作者： Gavrić, Katarina Ćulibrk, Dubravko Mirković, Milan Crnojević, Vladimir Dept. of Industrial Engineering and Management Faculty of Technical Sciences Novi Sad Serbia Dept. of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences Novi Sad Serbia

ISBN: (纸本)9781457711312

An increasing amount of publicly available geo-referenced data enables the identification of patterns of behavior, habits and movements of people. This paper presents results of a case study analysis based on data ser containing publicly available geo-referenced videos downloaded from YouTube, tagged as recorded in Africa. Our goal was to determine major routes of movement across the continent, to determine when people start their trip and the basic means of transportation used. The paper presents results of the analysis conducted on 113.157 unique YouTube records. We were able to identify major travel routes, directions, carriers and even flights favored by people traveling across Africa, information that is potentially valuable to disease outbreak management, airline industry, etc. © 2011 IEEE.

关键词： data visualization

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Sparsification of influence networks 11

Sparsification of influence networks

引用

17th ACM SIGKDD International conference on Knowledge Discovery and data Mining, KDD 2011

作者： Mathioudakis, Michael Bonchi, Francesco Castillo, Carlos Gionis, Aristides Ukkonen, Antti Computer Science Department University of Toronto Canada Yahoo Research Barcelona Spain

ISBN: (纸本)9781450308137

We present Spine, an efficient algorithm for finding the "backbone" of an influence network. Given a social graph and a log of past propagations, we build an instance of the independent-cascade model that describes the propagations. We aim at reducing the complexity of that model, while preserving most of its accuracy in describing the data. We show that the problem is inapproximable and we present an optimal, dynamic-programming algorithm, whose search space, albeit exponential, is typically much smaller than that of the brute force, exhaustive-search approach. Seeking a practical, scalable approach to sparsification, we devise Spine, a greedy, efficient algorithm with practically little compromise in quality. We claim that sparsification is a fundamental datareduction operation with many applications, ranging from visualization to exploratory and descriptive data analysis. As a proof of concept, we use Spine on real-world datasets, revealing the backbone of their influence-propagation networks. Moreover, we apply Spine as a pre-processing step for the influence-maximization problem, showing that computations on sparsified models give up little accuracy, but yield significant improvements in terms of scalability. Copyright 2011 ACM.

关键词： data visualization

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Visualizing collaboration and influence in the open-source software community

Visualizing collaboration and influence in the open-source s...

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8th Working conference on Mining Software Repositories, MSR 2011, Co-located with ICSE 2011

作者： Heller, Brandon Marschner, Eli Rosenfeld, Evan Heer, Jeffrey Stanford University United States

ISBN: (纸本)9781450305747

We apply visualization techniques to user profiles and repository metadata from the GitHub source code hosting service. Our motivation is to identify patterns within this development community that might otherwise remain obscured. Such patterns include the effect of geographic distance on developer relationships, social connectivity and influence among cities, and variation in projectspecific contribution styles (e.g., centralized vs. distributed). Our analysis examines directed graphs in which nodes represent users' geographic locations and edges represent (a) follower relationships, (b) successive commits, or (c) contributions to the same project. We inspect this data using a set of visualization techniques: geo-scatter maps, small multiple displays, and matrix diagrams. Using these representations, and tools based on them, we develop hypotheses about the larger GitHub community that would be difficult to discern using traditional lists, tables, or descriptive statistics. These methods are not intended to provide conclusive answers;instead, they provide a way for researchers to explore the question space and communicate initial insights. © 2011 ACM.

关键词： visualization

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Multi-Dimensional Transfer Functions for Effective visualization of Streaming Ultrasound and Elasticity Images

Multi-Dimensional Transfer Functions for Effective Visualiza...

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conference on Medical Imaging 2011 - visualization, Image-Guided Procedures, and Modeling

作者： Mann, David Caban, Jesus J. Stolka, Phillip J. Boctor, Emad M. Yoo, Terry S. NIH Natl Lib Med Bethesda MD 20892 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9780819485069

The low-cost and minimum health risks associated with ultrasound (US) have made ultrasonic imaging a widely accepted method to perform diagnostic and image-guided procedures. Despite the existence of 3D ultrasound probes, most analysis and diagnostic procedures are done by studying the B-mode images. Currently, multiple ultrasound probes include 6-DOF sensors that can provide positioning information. Such tracking information can be used to reconstruct a 3D volume from a set of 2D US images. Recent advances in ultrasound imaging have also shown that, directly from the streaming radio frequency (RF) data, it is possible to obtain additional information of the anatomical region under consideration including the elasticity properties. This paper presents a generic framework that takes advantage of current graphics hardware to create a low-latency system to visualize streaming US data while combining multiple tissue attributes into a single illustration. In particular, we introduce a framework that enables real-time reconstruction and interactive visualization of streaming data while enhancing the illustration with elasticity information. The visualization module uses two-dimensional transfer functions (2D TFs) to more effectively fuse and map B-mode and strain values into specific opacity and color values. On commodity hardware, our framework can simultaneously reconstruct, render, and provide user interaction at over 15 fps. Results with phantom and real-world medical datasets show the advantages and effectiveness of our technique with ultrasound data. In particular, our results show how two-dimensional transfer functions can be used to more effectively identify, analyze and visualize lesions in ultrasound images.

关键词： visualization Ultrasound Elasticity Imaging

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Visualizing Frequent Patterns in Large Multivariate Time Series

Visualizing Frequent Patterns in Large Multivariate Time Ser...

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conference on visualization and data analysis 2011

作者： Hao, M. Marwah, M. Janetzko, H. Sharma, R. Keim, D. A. Dayal, U. Patnaik, D. Ramakrishnan, N. Hewlett-Packard Labs. (United States) Univ. Konstanz (Germany) Virginia Polytechnic Institute and State Univ. (United States)

ISBN: (纸本)9780819484055

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

关键词： recurring patterns time series multivariate data motifs distortion merging

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