Visual data mining using principled projection algorithms and information visualization techniques

Dharmesh M. Maniyar, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

8 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the Twelfth ACM SIGKDD international conference on knowledge discovery and data mining
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Print)1-59593-339-5
Publication statusPublished - 1 Oct 2006


  • visual data mining, probabilistic projection algorithms, information visualization techniques

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