Description Usage Arguments Details Value Author(s) References Examples

A simple function to perform Model based cluster Analysis :

1 | ```
ModelCluster(Data, NewData = NULL, G, silent = FALSE, ...)
``` |

`Data` |
(dataframe) Data dataframe |

`NewData` |
(optional) (dataframe) New Data frame for which the class membership is requested |

`G` |
(optional) (numeric) No. of components to verify |

`silent` |
(optional) (logical) whether to print messages or not |

`...` |
(optional) additional arguments for the function |

The function implements Model based clustering in predictive framework. Model based clustering approaches provide a structured way of choosing number of clusters (C. Fraley & Raftery, 1998). Data are considered to be generated from a set of Gaussian distributions (components or clusters) i.e. as a mixture of these components (mixture models). Instead of using heuristics, model based clustering approximates Bayes factor (utilizing Bayesian information Criterion) to determine the model with the highest evidence (as provided by the data).

`class membership`

of the clustered `NewData`

Atesh Koul, C'MON unit, Istituto Italiano di Tecnologia

Han, J., Kamber, M., & Pei, J. (2012). Cluster Analysis. In Data Mining (pp. 443-495). Elsevier.

Fraley, C., & Raftery, a E. (1998). How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal, 41(8), 578-588.

1 2 3 4 5 6 | ```
# clustering kinematics data at 10% of movement
# not run
# cluster_time <- ModelCluster(KinData[,c(2,12,22,32,42,52,62,72,82,92,102,112)],G=1:12)
# Output:
# Performing Cluster analysis
# --cluster Results --
``` |

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