K-Means with simultaneous Outlier Detection

Version: 0.2.0
Date: 2022-04-11
Author: David Charles Howe


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)”, 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.


kmod(X, k = 3, l = 5)
use ?kmod for more details


X – matrix of numeric data or an object that can be coerced to such a matrix (such as a data frame with numeric columns only)
k – the number of clusters to find (default = 5)
l – the number of outliers to find (default = 0)