Information visualization and visual data mining

Note

A good survey paper to follow the trend of data visualization and mining. This paper provides a clear classification for visual data mining works.  The author describes: "The visual data exploration process be seen a hypothesis generation process". A visualization interface provides the user an overview of the dataset. Based on the insight, the user can explore/filter/verify the finding to answer the hypothesis, the hypothesis can be generated by user/statistics/machine learning. In another hand, a visual data exploration usually follows a three looping process: overview, filter, and detail-on-demand. The different insight will jump out while the user explores the data through designed interface.

A visual data mining has consisted with three components: 1) data type to be visualized:  1D, 2D, ND, Text and hypertext and algorithm data visualization; 2) visualization technique: standard 2/3D, geometrically transformed, icon-based, dense pixel and stacked display; 3) interaction and distortion technique: projection, filtering, zooming, interactive distortion, linking and brushing. Each categories is with a reference paper that worth to further reading.

Reference

1. Keim, Daniel A. "Information visualization and visual data mining." IEEE transactions on Visualization and Computer Graphics 8.1 (2002): 1-8.