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GrCount: Counting method for uncertain data.

Corrado Mencar¹, Witold Pedrycz^2,3

¹Department of Informatics, University of Bari "A. Moro", Bari, Italy.

|November 14, 2019

Summary

This summary is machine-generated.

This study introduces a novel method for counting uncertain data using Possibility Theory. The approach generates fuzzy intervals, offering exact or approximate counting for improved data analysis in fields like Bioinformatics.

Keywords:

Counting Fuzzy intervals GrCount Granular computing Possibility theory

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Area of Science:

Data Science
Bioinformatics
Computational Statistics

Background:

Handling uncertain data is a significant challenge in various scientific domains.
Traditional counting methods struggle with observations that lack precise referential association.
Possibility Theory offers a framework for modeling and quantifying uncertainty.

Purpose of the Study:

To develop a robust method for counting uncertain data.
To integrate Possibility Theory for representing and processing data uncertainty.
To provide a flexible counting approach with both exact and approximate variants.

Main Methods:

Data uncertainty is modeled using Possibility Theory, representing observations as possibility distributions.
A novel counting method is developed to incorporate these possibility distributions.
Two variants are proposed: exact counting (quadratic time) and approximate counting (linear time).

Main Results:

The method yields a fuzzy interval on the domain of natural numbers for each referent.
Exact counting provides the precise fuzzy interval, while approximate counting offers an estimate.
The method is demonstrated with a Python implementation and a Bioinformatics use case.

Conclusions:

The proposed method effectively counts uncertain data by leveraging Possibility Theory.
The availability of both exact and approximate counting variants enhances its applicability.
This approach offers a valuable tool for data analysis, particularly in fields with inherent data ambiguity.