Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Elements of information theory
Elements of information theory
GTM: the generative topographic mapping
Neural Computation
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
A Flexible Approach for Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
GTM: A Principled Alternative to the Self-Organizing Map
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization
IEEE Transactions on Knowledge and Data Engineering
Guiding local regression using visualisation
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
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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.