An agent enabling personalized learning in e-learning environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Integrating contextual information to enhance SOM-based text document clustering
Neural Networks - New developments in self-organizing maps
A Hybrid Layout Algorithm for Sub-Quadratic Multidimensional Scaling
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Fast multidimensional scaling through sampling, springs and interpolation
Information Visualization
ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection
Neural Processing Letters
Expert Systems with Applications: An International Journal
A Structural Adapting Self-organizing Maps Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
LazySOM: Image Compression Using an Enhanced Self-Organizing Map
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A swarm-inspired projection algorithm
Pattern Recognition
Applied Intelligence
A fast self-organizing map algorithm by using genetic selection
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Intelligently resolving point occlusion
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Hand gesture recognition based on SOM and ART
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Robustness analysis of the neural gas learning algorithm
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Implementing a chinese character browser using a topography-preserving map
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
IPSOM: a self-organizing map spatial model of how humans complete interlocking puzzles
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
The CoMIRVA toolkit for visualizing music-related data
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
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We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N×N neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine-tuned by the traditional Kohonen self-organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three-stage method, a topologically ordered feature map would be formed very quickly instead of requiring a huge amount of iterations to fine-tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm. Three data sets are utilized to illustrate the proposed method