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.
Package details |
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Author | Guang Yang |
Maintainer | Guang Yang <gyang274@gmail.com> |
License | MIT + file LICENSE |
Version | 0.1.5.4 |
URL | http://github.com/gyang274/skm |
Package repository | View on GitHub |
Installation |
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