Space Partitioning for Scalable K-Means

  title={Space Partitioning for Scalable K-Means},
  author={David Pettinger and Giuseppe Di Fatta},
  journal={2010 Ninth International Conference on Machine Learning and Applications},
K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as ‘brute force’ since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional… CONTINUE READING
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