Principal Direction Divisive Partitioning with kernels and k-means steering

@inproceedings{Zeimpekis2007PrincipalDD,
  title={Principal Direction Divisive Partitioning with kernels and k-means steering},
  author={Dimitrios Zeimpekis and Efstratios Gallopoulos},
  year={2007}
}
Clustering is a fundamental task in data mining. We propose, implement and evaluate several schemes that combine partitioning and hierarchical algorithms, specifically k-means and Principal Direction Divisive Partitioning (PDDP). Using available theory regarding the solution of the clustering indicator vector problem, we use 2-means to induce partitionings around fixed or varying cut-points. 2-means is applied either on the data or over its projection on a one-dimensional subspace. These… CONTINUE READING
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