In gyang274/skm: Selective k-Means

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

skm: 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.

Available on CRAN with vignettes.

An application that use skm to find the optimal location for building warehouses: OWL - Optimal Warehouse Locator.

gyang274/skm documentation built on May 17, 2019, 9:41 a.m.