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This article describes discovery visualization, a new visual data mining approach that has as a key element the heightened awareness of the user by the machine. Discovery visualization promotes the concept of continuous interaction with constant feedback between human and machine, and constant unfolding of the data. It does this by providing a combination of automated response and user selection to achieve and sustain animated action while the user explores time-dependent data. The process begins by automatically generating an overview using a fast clustering approach, where the clusters are then followed as time-dependent features. We applied discovery visualization to both test data and real application data. The results show that the method is accurate and scalable, and it offers a straightforward, error-based process for improving accuracy.
Jochen Katz, Frank Jiang, William Ribarsky, Aubrey Holland, "Discovery Visualization Using Fast Clustering", IEEE Computer Graphics and Applications, vol. 19, no. , pp. 32-39, September/October 1999, doi:10.1109/38.788796
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