kmodR: K-Means with Simultaneous Outlier Detection
An implementation of the 'k-means–' algorithm proposed
by Chawla and Gionis, 2013 in their paper,
"k-means– : A unified approach to clustering and outlier detection.
SIAM International Conference on Data Mining (SDM13)",
<doi:10.1137/1.9781611972832.21>
and using 'ordering' described by Howe, 2013 in the thesis,
Clustering and anomaly detection in tropical cyclones".
Useful for creating (potentially) tighter clusters than
standard k-means and simultaneously finding outliers inexpensively
in multidimensional space.
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