PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visual data mining using principled projection algorithms and information visualization techniques
Dharmesh Maniyar and Ian Nabney
In: the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 20-23 Aug 2006, Philadelphia, PA, USA.

Abstract

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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:2980
Deposited By:Dharmesh Maniyar
Deposited On:12 April 2007