Description Usage Arguments Value Author(s) References

Calculates the derivative for the cost function of the K-medoids algorithm for the given input. Should not be used by the user.

1 | ```
cost_function_derivative(w, var_list)
``` |

`w` |
The weight vector of length |

`var_list` |
A list of variables containing the following: data, the relevant data. method, the number of cluster k. bounds, the bounds on the weights. fk: see |

`Cost function derivative` |
The cost function derivative of this clustering with the given weights. A vector of length |

Jeroen van den Hoven

Clustering with optimised weights for Gower's metric: Using hierarchical clustering and Quasi-Newton methods to maximise the cophenetic correlation coefficient, Jeroen van den Hoven.

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