Hi,

I want to cluster my data points into K clusters using k-means algorithm,
and I want to use a deterministic (non-random) initialization scheme which
is also a "good" start. I found a paper by Ting Su and Jennifer Dy named "A
Deterministic Method for Initializing K-means Clustering" and I wonder if
there is a way in R to partition the points in the way that is described in
this paper.

"Starting from an initial cluster that contains the entire data set, the
method iteratively selects the cluster with the greatest SSE and divides it
into two subclusters using a hyperplane that passes through the cluster
centroid and is orthogonal to the principal eigenvector of the cluster
covariance matrix. This procedure is repeated until K clusters are
obtained."

If I get the final K centers from this partitioning, then I can give those
centers to R's kmeans algorithm to let it converge.

Is there a built-in R function to get such an initial partitioning?

Thanks!

-- 
-safiye

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