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)", 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.


kmodR

K-Means with simultaneous Outlier Detection

Version: 0.1.0 Date: 2015-03-26 Author: David Charles Howe [email protected]

Description:

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.

Usage:

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

Arguments

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)

News

Reference manual

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install.packages("kmodR")

0.1.0 by David Charles Howe, 4 years ago


Browse source code at https://github.com/cran/kmodR


Authors: David Charles Howe [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Suggests testthat


See at CRAN