Principal Direction Divisive Partitioning

  title={Principal Direction Divisive Partitioning},
  author={Daniel Boley},
  journal={Data Mining and Knowledge Discovery},
We propose a new algorithm capable of partitioning a set of documents or other samples based on an embedding in a high dimensional Euclidean space (i.e., in which every document is a vector of real numbers). The method is unusual in that it is divisive, as opposed to agglomerative, and operates by repeatedly splitting clusters into smaller clusters. The documents are assembled into a matrix which is very sparse. It is this sparsity that permits the algorithm to be very efficient. The… CONTINUE READING

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