A FAST K-MEANS TYPE CLUSTERING ALGORITHM
Date
1985-06-01
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Abstract
This paper describes a new $k$-means type clustering algorithm which
gives excellent results for a moderate computational cost. It is
particularly suitable for partitioning large data sets into a number
of clusters where the conventional $k$-means algorithm becomes
computationally unmanageable. While it does not guarantee to reach
a global optimum, its performance in practice is very good indeed,
as demonstrated by theoretical analysis and experiments on color
image data.
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Computer Science