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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

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Interactive exploration and analysis of Pathlines in Flow data

Interactive Exploration and Analysis of Pathlines in Flow Da...

引用

19th International conference in Central Europe on Computer Graphics, visualization and Computer Vision

作者： Lez, Alan Zajic, Andreas Matkovic, Kresimir Pobitzer, Armin Mayer, Michael Hauser, Helwig VRVis Res Ctr Vienna Austria Univ Bergen Bergen Norway AVL List GmbH Graz Austria

ISBN: (纸本)9788086943831

The rapid development of large-scale scientific computing nowadays allows to inherently respect the unsteady character of natural phenomena in computational flow simulation. With this new trend to more regularly consider time-dependent flow scenarios, an according new need for advanced exploration and analysis solutions emerges. In this paper, we now present three new concepts in pathline analysis which further improve the abilities of analysis: a multi-step analysis which helps to save time and space needed for computation, direct pathline brushing, and the use of pre-configured view arrangements. We have found that a clever combination of these three concepts with already existing methods creates a very powerful tool for pathline analysis. A solution that follows the concept of coordinated multiple views (CMV) with iterative composite brushing enables a quick information drill-down. We illustrate the usefulness of this approach in the context of an example from the automotive industry.

关键词： Interactive visual analysis Pathlines Flow visualization Exhaust Manifold

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CAVE-SOM: Immersive visual data Mining Using 3D Self-Organizing Maps

CAVE-SOM: Immersive Visual Data Mining Using 3D Self-Organiz...

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International Joint conference on Neural Networks (IJCNN)

作者： Wijayasekara, Dumidu Linda, Ondrej Manic, Milos Univ Idaho Dept Comp Sci Idaho Falls ID 83402 USA

ISBN: (纸本)9781424496365

data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.

关键词： 3D self-organizing maps 3D visual data mining framework data mining data visualization Humans Iris Neurons Three dimensional displays U-matrix visualization data histogram data mining data visualisation decision making dimensionality reduction human observer human perception immersive cave automated virtual environment immersive visual data mining immersive visualization input space view knowledge extraction machine learning multidimensional datasets self-organising feature maps self-organizing map algorithm wind-power generation data

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Character n-Gram Spotting in Document Images

Character n-Gram Spotting in Document Images

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11th International conference on Document analysis and Recognition (ICDAR)

作者： Praveen, Sudha M. Sankar, Pramod K. Jawahar, C. V. IIIT Hyderabad Ctr Visual Informat Technol Hyderabad Andhra Pradesh India

ISBN: (纸本)9780769545202

In this paper, we present a novel approach to search and retrieve from document image collections, without explicit recognition. Existing recognition-free approaches such as word-spotting cannot scale to arbitrarily large vocabulary and document image collections. In this paper we put forth a framework that overcomes three issues of word-spotting: i) retrieving word images not labeled during indexing, ii) allow for query and retrieval of morphological variations of words and iii) scale the retrieval to large collections. We propose a character n-gram spotting framework, where word-images are considered as a bag of visual n-grams. The character n-grams are represented in a visual-feature space and indexed for quick retrieval. In the retrieval phase, the query word is expanded to its constituent n-grams, which are used to query the previously built index. A ranking mechanism is proposed that combines the retrieval results from the multiple lists corresponding to each n-gram. The approach is demonstrated on a size-able collection of English and Malayalam books. With a mean AP of 0.64, the performance of the retrieval system was found to be very promising.

关键词： Word-Spotting Character n-Grams Recognition-free Scalability

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DCI 2011 - Proceedings of the International Workshop on data-Centric Interactions on the Web, in Conjunction with the 13th IFIP TC13 conference on Human-Computer-Interaction, INTERACT 2011

DCI 2011 - Proceedings of the International Workshop on Data...

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International Workshop on data-Centric Interactions on the Web, DCI 2011 - In Conjunction with the 13th IFIP TC13 conference on Human-Computer-Interaction, INTERACT 2011

The proceedings contain 5 papers. The topics discussed include: designing visual systems for social data analysis in open government applications;online banking customization via tag-based interaction;tFacet: hierarchical faceted exploration of semantic data using well-known interaction concepts;more!: mobile interaction with linked data;and interacting with semantic data by using X3S.

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HMI ring diagram analysis ii. data products

HMI ring diagram analysis II. Data products

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GONG2010-SoHO24 conference on A New Era of Seismology of the Sun and Solar-Like Stars

作者： Bogart, R. S. Baldner, C. Basu, S. Haber, D. A. Rabello-Soares, M. C. Stanford Univ Stanford CA 94305 USA Yale Univ New Haven CT USA Univ Colorado JILA Boulder CO 80309 USA

The combination of high resolution, spatial coverage, and continuity of photospheric Doppler and other data from HMI has allowed us to embark on a program of systematic exploration of solar subsurface flows and thermal structure variations using the technique of ring-diagram analysis on an unprecedented scale. There are two ring-diagrams pipelines, as described in [1]. In this paper we discuss the synoptic pipeline execution and describe the data being processed and produced.

关键词： Pipelines

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Online Learning for PLSA-Based visual Recognition

Online Learning for PLSA-Based Visual Recognition

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10th Asian conference on Computer Vision (ACCV)

作者： Xu, Jie Ye, Getian Wang, Yang Wang, Wei Yang, Jun Natl ICT Australia Sydney NSW Australia Univ New South Wales Sydney NSW 2052 Australia Canon Informat Syst Res Australia Sydney NSW Australia

ISBN: (纸本)9783642193088

Probabilistic Latent Semantic analysis (PLSA) is one of the latent topic models and it has been successfully applied to visual recognition tasks. However, PLSA models have been learned mainly in batch learning, which can not handle data that arrives sequentially. In this paper, we propose a novel on-line learning algorithm for learning the parameters of PLSA. Our contributions are two-fold: (i) an on-line learning algorithm that learns the parameters of a PLSA model from incoming data;(ii) a codebook adaptation algorithm that can capture the full characteristics of all the features during the learning. Experimental results demonstrate that the proposed algorithm can handle sequentially arriving data that batch PLSA learning cannot cope with, and its performance is comparable with that of the batch PLSA learning on visual recognition.

关键词： Learning algorithms

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Research of Indemnificatory Housing Object Divided Based on Cluster analysis

Research of Indemnificatory Housing Object Divided Based on ...

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18th International conference on Management Science and Engineering

作者： Cui Yu-ying Tian Jin-xin Li Zhi-qing Harbin Inst Technol Sch Management Harbin 150001 Peoples R China

ISBN: (纸本)9781457718861

Within the government vigorously promoting, indemnificatory housing achieved the positive results, but the division of the object of indemnificatory housing is still the difficulty of the policy-making. This article starts from the characteristic elements of the object of indemnificatory housing, using the theory of cluster analysis and SPSS software to classify the objects, and analysis of interview data. The results show that cluster analysis can divide the object of indemnificatory housing clearly. And the research is the innovative exploration of method on objects of housing security.

关键词： clustering analysis indemnificatory housing level division the object of indemnificatory housing

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Web-based atmospheric nucleation data management and visualization

Web-based atmospheric nucleation data management and visuali...

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2nd International conference on Networking and Distributed Computing, ICNDC 2011

作者： Zhu, Kai Song, Huaguang Gao, Jinzhu Department of Computer Science University of the Pacific Stockton CA 95211 United States

ISBN: (纸本)9780769544274

Nucleation phenomena play a crucial role in plenty of atmospheric and technological processes. Being short of efficient visual data exploration tools has given rise to trouble understanding atmospheric nucleation processes. In this paper, we utilize the knowledge of hypertree visualization to enhance our previously developed web-based visualization and analysis tool that allows remote users to effectively mine the wealth of particle-based nucleation simulation data. Our goal is to speed up knowledge discovery and improve users' productivity through effective visual data mining techniques and more friendly user interface design. Meanwhile, we developed a feasible parallel computing solution to overcome the slow response due to expensive large data pre-processing. © 2011 IEEE.

关键词： visualization

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TopicView: visually Comparing Topic Models of Text Collections

TopicView: Visually Comparing Topic Models of Text Collectio...

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23rd IEEE International conference on Tools with Artificial Intelligence (ICTAI)

作者： Crossno, Patricia J. Wilson, Andrew T. Shead, Timothy M. Dunlavy, Daniel M. Sandia Natl Labs Scalable Anal & Visualizat POB 5800 Albuquerque NM 87185 USA Sandia Natl Labs Data Anal & Informat Albuquerque NM 87185 USA

ISBN: (纸本)9780769545967

We present TopicView, an application for visually comparing and exploring multiple models of text corpora. TopicView uses multiple linked views to visually analyze both the conceptual content and the document relationships in models generated using different algorithms. To illustrate TopicView, we apply it to models created using two standard approaches: Latent Semantic analysis (LSA) and Latent Dirichlet Allocation (LDA). Conceptual content is compared through the combination of (i) a bipartite graph matching LSA concepts with LDA topics based on the cosine similarities of model factors and (ii) a table containing the terms for each LSA concept and LDA topic listed in decreasing order of importance. Document relationships are examined through the combination of (i) side-by-side document similarity graphs, (ii) a table listing the weights for each document's contribution to each concept/topic, and (iii) a full text reader for documents selected in either of the graphs or the table. We demonstrate the utility of TopicView's visual approach to model assessment by comparing LSA and LDA models of two example corpora.

关键词： text analysis visual model analysis latent semantic analysis latent dirichlet allocation

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CommentSpace: Structured support for collaborative visual analysis 11

CommentSpace: Structured support for collaborative visual an...

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Proceedings of the SIGCHI conference on Human Factors in Computing Systems

作者： Willett, Wesley Heer, Jeffrey Hellerstein, Joseph M. Agrawala, Maneesh Computer Science Division University of California Berkeley United States Computer Science Department Stanford Univeristy United States

ISBN: (纸本)9781450302289

Collaborative visual analysis tools can enhance sensemaking by facilitating social interpretation and parallelization of effort. These systems enable distributed exploration and evidence gathering, allowing many users to pool their effort as they discuss and analyze the data. We explore how adding lightweight tag and link structure to comments can aid this analysis process. We present CommentSpace, a collaborative system in which analysts comment on visualizations and websites and then use tags and links to organize findings and identify others' contributions. In a pair of studies comparing CommentSpace to a system without support for tags and links, we find that a small, fixed vocabulary of tags (question, hypothesis, to-do) and links (evidence-for, evidence-against) helps analysts more consistently and accurately classify evidence and establish common ground. We also find that managing and incentivizing participation is important for analysts to progress from exploratory analysis to deeper analytical tasks. Finally, we demonstrate that tags and links can help teams complete evidence gathering and synthesis tasks and that organizing comments using tags and links improves analytic results. Copyright 2011 ACM.

关键词： visualization

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