Selective k-Means
col_max_idx
col_max_val
col_min_idx
col_min_val
col_rgn_val
dist_wlatlng
dist_wlatlng_cpp
skm_gdp_cpp
skm_minmax_cpp
skm_mlp_cpp
skm_mls
skm_mls_cpp
skm_rgi_cpp
skm_rgs_cpp
skm_sgl_cpp
skmRpl_mlp_cpp
skmSolution
stratified_sampling
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.