The LBG-U Method for Vector Quantization – an Improvement over LBGInspired from Neural Networks
Neural Processing Letters
A local search approximation algorithm for k-means clustering
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometrySoCG2002
Iterative shrinking method for clustering problems
Pattern Recognition
Improvement of the k-means clustering filtering algorithm
Pattern Recognition
Clustering by competitive agglomeration
Pattern Recognition
A fast exact GLA based on code vector activity detection
IEEE Transactions on Image Processing
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Random swap-based clustering is very simple to implement and guaranteed to find the correct clustering if iterated long enough. However, its quadratic dependency on the number of clusters can be too slow in case of some data sets. Deterministic selection of the swapped prototype can speed-up the algorithm but only if the swap can be performed fast enough. In this work, we introduce an efficient implementation of the swap-based heuristic and compare its time-distortion efficiency against random and deterministic variants of the swap-based clustering, and repeated k-means.