Does organisation by similarity assist image browsing?
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Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Best Increments for the Average Case of Shellsort
FCT '01 Proceedings of the 13th International Symposium on Fundamentals of Computation Theory
Fast multidimensional scaling through sampling, springs and interpolation
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IV '08 Proceedings of the 2008 12th International Conference Information Visualisation
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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The Journal of Machine Learning Research
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IEEE Transactions on Visualization and Computer Graphics
Self organization of a massive document collection
IEEE Transactions on Neural Networks
A scalable parallel force-directed graph layout algorithm
EG PGV'08 Proceedings of the 8th Eurographics conference on Parallel Graphics and Visualization
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This paper presents the Self-Sorting Map (SSM), a novel algorithm for organizing and visualizing data. Given a set of data items and a dissimilarity measure between each pair of them, the SSM places each item into a unique cell of a structured layout, where the most related items are placed together and the unrelated ones are spread apart. The algorithm nicely integrates ideas from dimension reduction techniques, sorting algorithms, and data clustering approaches. Instead of solving the continuous optimizing problem as other dimension reduction approaches do, the SSM transforms it into a discrete labeling problem. As a result, it can organize a set of data into a structured layout without overlapping, providing a simple and intuitive presentation. Experiments on different types of data show that the SSM can be applied to a variety of applications, ranging from visualizing semantic relatedness between articles to organizing image search results based on visual similarities. Our current SSM implementation using Java is fast enough for interactively organizing datasets with hundreds of entries.