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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.

Versions across snapshots

VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 kmodR_0.2.0.tar.gz 22.4 KiB
0.2.0 rolling linux/noble R-4.5 kmodR_0.2.0.tar.gz 22.3 KiB
0.2.0 rolling source/ R- kmodR_0.2.0.tar.gz 11.1 KiB
0.2.0 latest linux/jammy R-4.5 kmodR_0.2.0.tar.gz 22.4 KiB
0.2.0 latest linux/noble R-4.5 kmodR_0.2.0.tar.gz 22.3 KiB
0.2.0 latest source/ R- kmodR_0.2.0.tar.gz 11.1 KiB
0.2.0 2026-04-26 source/ R- kmodR_0.2.0.tar.gz 11.1 KiB
0.2.0 2026-04-23 source/ R- kmodR_0.2.0.tar.gz 11.1 KiB
0.2.0 2026-04-09 windows/windows R-4.5 kmodR_0.2.0.zip 24.8 KiB
0.2.0 2025-04-20 source/ R- kmodR_0.2.0.tar.gz 11.1 KiB

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