Web Intelligence
A Neural-Network-Based Geographic Tendency Visualization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Application of neural networks and Kano's method to content recommendation in web personalization
Expert Systems with Applications: An International Journal
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web mining in soft computing framework: relevance, state of the art and future directions
IEEE Transactions on Neural Networks
A Forward-Constrained Regression Algorithm for Sparse Kernel Density Estimation
IEEE Transactions on Neural Networks
Clustering and Visualizing Geographic Data Using Geo-tree
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
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Visualization of geographic log-data is one of the key issues on geovisualization, which is defined as a research field of visualizing geographic information. This paper aims to visualize them interactively using graphics like thermograph, mashuped with interactive mapping system (IMS), such as Google Map. While conventional researches employ probability density function estimation algorithms, the problems are twofold. One is that the focused data should be analyzed rapidly online during the interaction between systems and users, for the map size and location can be changed flexibly with IMS. The other is that focused data may be sparse when the map is zoomed in. In general, EM algorithm, a commonly-used probabilistic density approximator, is not robust to sparseness and it takes long time for model construction. Parzen window is also a simple, well-known technique but it requires many kernels that make calculation costs high. The proposed method is a novel, simple kernel density estimator which is fast for model construction with high robustness to sparse data. The proposed method is based on Parzen window and employs a clustering algorithm inspired by fuzzy ART (Adaptive Resonance Theory) to reduce kernels. From the experimental results, estimation accuracy excels the conventional methods with various benchmarking models.