We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.
|Title of host publication||Proceedings of the Twelfth ACM SIGKDD international conference on knowledge discovery and data mining|
|Place of Publication||United States|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||6|
|Publication status||Published - 1 Oct 2006|
- visual data mining, probabilistic projection algorithms, information visualization techniques