Principal Direction Divisive Partitioning
1997
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Principal Direction Divisive Partitioning
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1997
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Abstract
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 splits are not based on any distance or similarity measure.
The documents are assembled in to a matrix which is very sparse. It is this sparsity
that permits the algorithm to be very efficient. The performance of the method is
illustrated with a set of text documents obtained from the World Wide Web. Some
possible extensions are proposed for further investigation.
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This research was partially supported by NSF grants CCR-9405380 and CCR-9628786.
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Boley, Daniel. (1997). Principal Direction Divisive Partitioning. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215341.
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