To see, or not to see— is That the query?
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
New paradigms in information visualization (poster session)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
VisDB: Database Exploration Using Multidimensional Visualization
IEEE Computer Graphics and Applications
30 Years of Multidimensional Multivariate Visualization
Scientific Visualization, Overviews, Methodologies, and Techniques
Hierarchical Parallel Coordinates for Exploration of Large Datasets
VISUALIZATION '99 Proceedings of the 10th IEEE Visualization 1999 Conference (VIS '99)
Interactive Information Visualization of a Million Items
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
An interactive visualization environment for data exploration using points of interest
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
An artificial ants model for fast construction and approximation of proximity graphs
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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We present in this paper a new method for the visual exploration of large data sets with up to one million of objects. We highlight some limitations of the existing visual methods in this context. Our approach is based on previous systems like Vibe, Sqwid or Radviz which have been used in information retrieval: several data called points of interest (POIs) are placed on a circle. The remaining large amount of data is displayed within the circle at locations which depend on the similarity between the data and the POIs. Several interactions with the user are possible and ease the exploration of the data. We highlight the visual and computational properties of this representation: it displays the similarities between data in a linear time, it allows the user to explore the data set and to obtain useful information. We show how it can be applied to standard 'small' databases, either benchmarks or real world data. Then we provide results on several large, real or artificial, data sets with up to one million data. We describe then both the successes and limits of our method.