On Neutrosophy logic for Multiple Correlation Analysis in Fuzzy Uncertain Database

Abstract

The primary goal of this paper is to introduce a novel method for mining frequent and 
interesting items by incorporating correlation analysis between two items in an uncertain 
transactional database using the OWA operator. The work is further expanded by proposing two 
additional methods for mining frequent and interesting items, utilizing fuzzy means and the OWA 
operator along with multiple correlation analysis for more than two items in uncertain transactional 
databases. The effectiveness of the proposed methods is evaluated by running the algorithms on 
both standard and example datasets, with results compared to the traditional probabilistic approach 
for identifying frequent and interesting items through multiple correlation. While multiple 
correlation analysis highlights the relationships of interestingness and uninterestingness between 
items, the OWA operator enhances the results when combined with fuzzy means and probabilistic 
methods. Additionally, the paper suggests a future research direction using Neutrosophy logic, 
which is anticipated to open new avenues for further exploration in this field.

DOI:10.5281/zenodo.13955955