Description Usage Arguments Details Value How does this algorithm work? Examples

Discovering the behavior of variables in a set of clustering packages based on evaluation metrics.

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`path` |
The path of file. |

`df` |
data matrix or data frame, or dissimilarity matrix. |

`packages` |
character vector with the packets running the algorithm. |

`algorithm` |
character vector with the algorithms implemented within the package. |

`min` |
An integer with the minimum number of clusters This data is necessary to indicate the minimum number of clusters when grouping the data. The default value is |

`max` |
An integer with the maximum number of clusters. This data is necessary to indicate the maximum number of clusters when grouping the data. The default value is |

`metrics` |
Character vector with the metrics implemented to evaluate the distribution of the data in clusters. |

`variables` |
an boolean which indicates that if we want to show as a result the variables of the datasets or the numerical value of the calculation of the metrics. The default value is |

The operation of this algorithm is to evaluate how the variables of a dataset or a set of datasets behave in
different grouping algorithms. To do this, it is necessary to indicate the type of evaluation you want to make on the
distribution of the data. To be able to execute the algorithm it is necessary to indicate the number of clusters
`min`

and `max`

, the algorithms `algorithm`

or packages `packages`

that we want to cluster,
the metrics `metrics`

and if we want that the results of evaluation are the own classified variables or numerical values `variables`

.

a matrix with the result of running all the metrics of the algorithms contained in the packages we indicated. We also obtain information with the types of metrics, algorithms and packages executed.

result It is a list with the algorithms, metrics and variables defined in the execution of the algorithm.

hasInternalMetrics Boolean field to indicate if there are internal metrics such as: dunn, silhoutte and connectivity.

hasExternalMetrics Boolean field to indicate if there are external metrics such as: precision, recall, f-measure, entropy, variation information and fowlkes-mallows.

algorithms_execute Character vector with the algorithms executed. These algorithms have been mentioned in the definition of the parameters.

measures_execute Character vector with the measures executed. These measures have been mentioned in the definition of the parameters.

This algorithm improves and complements existing implementations of clustering algorithms.

The approaches that exist, are many algorithms that run parallel to the algorithms, without being able to be compared between them. In addition, it was necessary to indicate which variable of the dataset is required to be executed. In addition, depending on the package there are some implementations or others to evaluate the groupings of data, so it is sometimes complicated to compare the groupings between different packages.

With this algorithm we can solve the problems mentioned above and determine which algorithm has the best behavior for the set of variables as well as the most efficient number of clusters.

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