Comparing Circular Histograms by Using Modulo Similarity and Maximum Pair-Assignment Compatibility Measure

@article{Luukka2017ComparingCH,
  title={Comparing Circular Histograms by Using Modulo Similarity and Maximum Pair-Assignment Compatibility Measure},
  author={Pasi Luukka and Mikael Collan},
  journal={Int. J. Comput. Intell. Syst.},
  year={2017},
  volume={10},
  pages={1-12}
}
Histograms are an intuitively understandable tool for graphically presenting frequency data that is available for and useful in modern data-analysis, this also makes comparing histograms an interesting field of research. The concept of similarity and similarity measures have been gaining in importance, because similarity and similarity measures can be used to replace the simpler distance measures in many data-analysis applications. In this paper we concentrate on circular histograms that are… 

Figures and Tables from this paper

Similarity of histograms and circular histograms from interval and fuzzy data

  • J. MezeiP. LuukkaM. Collan
  • Computer Science
    2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)
  • 2017
TLDR
Various similarity measures that can be used to assess the similarity of histograms are defined and a bin-based similarity approach applicable to circular histograms is proposed.

References

SHOWING 1-10 OF 20 REFERENCES

Modulo similarity in comparing histograms

TLDR
This work presents two new measures, the "modulo similarity" measure and the "maximum pair assignment compatibility" measure, which do not use PDF conversion, vector-based approaches, in the comparison of histograms, but concentrate on the data samples used to form histograms.

On measuring the distance between histograms

A Relevance-Based Learning Model of Fuzzy Similarity Measures

TLDR
A general framework of fuzzy logical-based similarity measures based on T-equalities that are derived from residual implication functions is proposed and a model that allows us to learn the parametric similarity measures is introduced.

A metric for distributions with applications to image databases

TLDR
This paper uses the Earth Mover's Distance to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays, and proposes a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.

Similarity relations and fuzzy orderings

Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers

TLDR
The proposed fuzzy risk analysis method is more flexible and more intelligent than the existing methods due to the fact that it considers the degrees of confidence of decisionmakers' opinions.

Similarity in fuzzy reasoning

TLDR
The connection between indistinguishability modelled by fuzzy equivalence relations and fuzzy sets is elucidated and it is shown that the indistinguishedability inherent to fuzzy sets can be computed and that this indistinguishesability cannot be overcome in approximate reasoning.

The Divergence and Bhattacharyya Distance Measures in Signal Selection

TLDR
This partly tutorial paper compares the properties of an often used measure, the divergence, with a new measure that is often easier to evaluate, called the Bhattacharyya distance, which gives results that are at least as good and often better than those given by the divergence.