Description Usage Arguments Details Value References Examples

Computes some criteria for comparing two classifications of the data points.

1 | ```
compareCluster(class1, class2)
``` |

`class1` |
A numeric or character vector of class labels. |

`class2` |
A numeric or character vector of class labels. Must be same length of |

The Jaccard, Rand and adjusted Rand indices measure the agreement between two partitions of the units. These indices vary in the interval *[0,1]* and a value of 1 corresponds to a perfect correspondence. Note that sometimes the adjusted Rand index could take negative values (see Hubert, Arabie, 1985). The variation of information is a measure of the distance between the two clusterings and a small value is indication of closeness.

A list containing:

`tab` |
The confusion matrix between the two clusterings. |

`jaccard` |
Jaccard index. |

`RI` |
Rand index. |

`ARI` |
Adjusted Rand index. |

`varInfo` |
Variation of information between the two clusterings. |

Hubert, L. and Arabie, P. (1985). Comparing partitions. *Journal of Classification*, 2193-218.

Meila, M. (2007). Comparing clusterings - an information based distance. *Journal of Multivariate Analysis*, 98, 873-895.

1 2 3 4 | ```
cl1 <- sample(1:3, 100, replace = TRUE)
cl2 <- sample(letters[1:4], 100, replace = TRUE)
compareCluster(cl1, cl2)
compareCluster(cl1, cl1) # perfect matching
``` |

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