|InterJournal Complex Systems, 640
|Manuscript Number: |
Submission Date: 40405
|Unsupervised Partitioning of Data|
Subject(s): CX.07, CX.11, CX.65
Category: Brief Article
This study describes an unsupervised general methodology for partitioning data into clusters. The notion of clusters is based on information-theoretic terms for the entropy of the partitions given the data. The model produces a hierarchy of clustering, where each partition is expressed as a mixture of a set of Gaussian kernel functions. It can deal with cluster of general shapes and sizes.
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