GT-Miner: a graph-theoretic data miner, viewer, and model processor |
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Authors: | Douglas E Brown Amy J Powell Ignazio Carbone Ralph A Dean |
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Affiliation: | Center for Integrated Fungal Research (CIFR), Department of Plant Pathology, Box 7251, North Carolina State University, Raleigh, NC 27695 7251 |
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Abstract: | Inexpensive computational power combined with high-throughput experimental platforms has created a wealth of biological information requiring analytical tools and techniques for interpretation. Graph-theoretic concepts and tools have provided an important foundation for information visualization, integration, and analysis of datasets, but they have often been relegated to background analysis tasks. GT-Miner is designed for visual data analysis and mining operations, interacts with other software, including databases, and works with diverse data types. It facilitates a discovery-oriented approach to data mining wherein exploration of alterations of the data and variations of the visualization is encouraged. The user is presented with a basic iterative process, consisting of loading, visualizing, transforming, and then storing the resultant information. Complex analyses are built-up through repeated iterations and user interactions. The iterative process is optimized by automatic layout following transformations and by maintaining a current selection set of interest for elements modified by the transformations. Multiple visualizations are supported including hierarchical, spring, and force-directed self-organizing layouts. Graphs can be transformed with an extensible set of algorithms or manually with an integral visual editor. GT-Miner is intended to allow easier access to visual data mining for the non-expert. |
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Keywords: | graph theory data mining visualization information visualization |
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