Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
FREM: fast and robust EM clustering for large data sets
Proceedings of the eleventh international conference on Information and knowledge management
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Scalable model-based cluster analysis using clustering features
Pattern Recognition
A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
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We present an algorithm for generating a mixture model from data set by performing a single pass over the data. The method is applicable when the entire data is not available at the same time in the main memory. We use Gaussian mixture model but the algorithm can be adapted to other types of models, too. We also outline a post processing method, which can iteratively reduce the size of the model obtained by the single-pass algorithm. This will result in a model with fewer components, but with approximately the same representation accuracy than the result of the original model from the single-pass algorithm.