{'fraction_non_alphanumeric': 0.014176663031624863, 'fraction_numerical': 0.0, 'mean_word_length': 5.0394736842105265, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Some Measures Relating Partitions Useful for Computational Intelligence

We investigate a number of measures relating partitions. One class of measures we consider are congruence measures. These measures are used to calculate the similarity between two partitionings. We provide a number of examples of this type of measure. Another class of measures we investigate are prognostication measures. This type of measure, closely related to a concept of containment between partitions, is useful in indicating how well knowledge of an objects class in one partitioning indicates its class in a second partitioning. We apply our measures to some data mining applications. One example is in choosing the appropriate level of a concept hierarchy. We also introduce a measure of the non-specificity of a partition. This measures a feature of a partition related to the granularity of the constituent classes of the partition.