Modulo similarity in comparing histograms

DOI

10.2991/ifsa-eusflat-15.2015.57How to use a DOI?

Keywords

Histograms, comparison, ukasiewicz logic, similarity, modulo similarity.

Abstract

Histograms are a tool for graphical representation of frequency data and thus helpful in creating a fast understanding of, e.g., contents of frequency data. Comparing histograms is topic of increasing importance due to an increase in the availability of data sets containing frequency information. Automatic data collection from “everywhere” has made collection of frequency data very common. As many different types of similarities exist, our focus is on ukasiewicz logic-based similarity and we present two new measures, the “modulo similarity” measure and the “maximum pair assignment compatibility” measure. These measures do not use PDF conversion, or vector-based approaches, in the comparison of histograms, but concentrate on the data samples used to form histograms. We illustrate the usefulness of these measures with numerical examples.

Copyright

© 2015, 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  - CONF
AU  - Pasi Luukka
AU  - Mikael Collan
PY  - 2015/06
DA  - 2015/06
TI  - Modulo similarity in comparing histograms
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 393
EP  - 397
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.57
DO  - 10.2991/ifsa-eusflat-15.2015.57
ID  - Luukka2015/06
ER  -