Modified genetic algorithm-based clustering for probability density functions

ABSTRACT

This article modifies two internal validity measures and applies them to evaluate the quality of clustering for probability density functions (pdfs). Based on these measures, we propose a new modified genetic algorithm called GA-CDF to establish the suitable clusters for pdfs. The proposed algorithm is tested by four numerical examples including two synthetic data sets and two real data sets. These examples illustrate the superiority of proposed algorithm over some existing algorithms in evaluating the internal or external validity measures. It demonstrates the feasibility and applicability of the GA-CDF for practical problems in data mining.

KEYWORDS:

• Clustering
• density function
• genetic algorithm
• validity measure
• optimization

MATHEMATICS SUBJECT CLASSIFICATION:

• 62H30
• 68T10

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Trung Nguyen-Thoi http://orcid.org/0000-0001-7985-6706