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Clustering plays an important role in many large-scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem,(More)
Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we(More)
We present an interactive visual analytics system for classification, iVisClassifier, based on a supervised dimension reduction method, linear discriminant analysis (LDA). Given high-dimensional data and associated cluster labels, LDA gives their reduced dimensional representation, which provides a good overview about the cluster structure. Instead of a(More)
This article describes the sense-making process we applied to solve the VAST 2010 Mini Challenge 1 using the visual analytics system Jigsaw. We focus on Jigsaw's data ingest and evidence marshalling features and discuss how they are beneficial for a holistic sense-making experience. 1 INTRODUCTION We used the Jigsaw system [3] to solve the VAST 2010 Mini(More)
Exploratory search and information-seeking support systems attempt to go beyond simple information retrieval and assist people with exploration, investigation, learning and understanding activities on document collections. In this work we integrate several computational text analysis techniques, including document sum-marization, document similarity,(More)
Our visual analytics tool GeneTracer, developed for the VAST 2010 genetic sequence mini challenge, visualizes gene sequences of current outbreaks and native sequences along with disease characteristics. We successfully used GeneTracer in combination with data mining techniques to solve the challenge. 1 PROBLEM OVERVIEW The task of the VAST 2010 Mini(More)
Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through: The papers in this book(More)
Low power "helper" cores have been increasingly included on application processors to accomplish low intensity tasks such as music playing and motion sensing with minimum energy consumption. Recently, Guimbretière et al. [1] demonstrated that such helper cores can also be used to execute simple user interface tasks. We revisit this approach by(More)
We created a visual analytics tool called EpiDetector for the VAST 2010 Mini Challenge 2. The system visualizes hospitalization records across different cities involved in an epidemic outbreak. We began our analysis process by cleaning the data and aggregating many different symptoms into eight main syndromes. EpiDetector then presents hospital admittances,(More)