Some Measures Relating Partitions Useful for Computational Intelligence

DOI

10.2991/ijcis.2008.1.1.1How to use a DOI?

Abstract

SOME MEASURES RELATING PARTITIONS USEFUL FOR COMPUTATIONAL INTELLIGENCE We investigate a number of measures relating partitions. One class of measures we consider are congru- ence 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 prognosti- cation 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 choos- ing 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.

Copyright

© 2008, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ronald R. Yager
PY  - 2008
DA  - 2008/01/01
TI  - Some Measures Relating Partitions Useful for Computational Intelligence
JO  - International Journal of Computational Intelligence Systems
SP  - 1
EP  - 18
VL  - 1
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2008.1.1.1
DO  - 10.2991/ijcis.2008.1.1.1
ID  - Yager2008
ER  -