A
computational study of several relocation methods for k-means algorithms
Summary
The purpose of this paper is to
report and discuss the results of an empirical investigation of several
techniques used by k-means algorithms (based on the Friedman-Rubin approach)
to move entities from one cluster to another. Most of these procedures
differ basically in the number of criterion evaluations required to reach
an optimum and the accuracy of this optimum. The prime objective of the
current research study has been to establish the relative merits of seventeen
combinatorial passes by comparing them across a variety of artificial data
sets. The experimental results suggest that a direct and efficient search
which moves down the steepest permissible direction globally outperforms
both simple and more sophisticated reassignment methods in terms of grouping
efficacy and numerical efficiency.
keywords
non-hierarchical classification,
iterative partitioning, combinatorial optimization
Pattern recognition, 2003, Vol. 36, n.12, 2955-2966.
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