Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
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
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Adaptive mixtures of local experts
Neural Computation
Visual data mining using principled projection algorithms and information visualization techniques
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.