Normalized Symmetric Mutual Information

(8.1) |

Also,

(8.2) |

and

(8.3) |

So

Since , a tight upper bound on is given by . Thus, a worst case upper bound for all possible labelings A (with labels from 1 to ) and categorizations (with labels from 1 to ) is given by

Hence, we define [0,1]-normalized mutual information-based quality as

(8.6) |

Using

(8.7) |

and approximating probabilities with frequency counts delivers our quality measure :

(8.8) |

Basic simplifications yield:

This derivation is used to obtain equations 4.25 and 5.2.

Instead of using the worst case upper bound for all possible labelings and categorizations, one can assume that the categorization priors are given. This allows a less aggressive denominator for normalization: One can use the actual entropies and from the labelings in equation B.4. However, this results in a bias towards high .

Another variant of normalization uses the actual entropies and instead of and in equation B.5. For the results presented in this dissertation, we will use the worst case normalization (equation B.9).