Selective k-Means

Algorithms for solving selective k-means problem, which is defined as finding k rows in an m x n matrix such that the sum of each column minimal is minimized. In the scenario when m == n and each cell value in matrix is a valid distance metric, this is equivalent to a k-means problem. The selective k-means extends the k-means problem in the sense that it is possible to have m != n, often the case m < n which implies the search is limited within a small subset of rows. Also, the selective k-means extends the k-means problem in the sense that the instance in row set can be instance not seen in the column set, e.g., select 2 from 3 internet service provider (row) for 5 houses (column) such that minimize the overall cost (cell value) - overall cost is the sum of the column minimal of the selected 2 service provider.


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install.packages("skm")

0.1.5.4 by Guang Yang, 7 months ago


http://github.com/gyang274/skm


Report a bug at http://github.com/gyang274/skm/issues


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


Authors: Guang Yang


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports methods, plyr, Rcpp, RcppParallel

Depends on magrittr, data.table

Suggests knitr, rmarkdown

Linking to Rcpp, RcppArmadillo, RcppParallel

System requirements: GNU make


See at CRAN