Rough set based information theoretic approach for clustering uncertain categorical data.

ABSTRACT:

Motivation

Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability.

Problem statement

The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability espe

SUBMITTER: Uddin J 

PROVIDER: S-EPMC9106167 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature