Cluster optimization for exponential, right - triangular, and uniformly distributed data

Bulivou, Gabiriele and Reddy, Karuna G. and Khan, Mohammad G.M. (2025) Cluster optimization for exponential, right - triangular, and uniformly distributed data. IEEE Access, 13 . pp. 36273-36284. ISSN 2169-3536

Text - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Cluster analysis aims to categorize data objects into cohesive groups based on their intrinsic characteristics, often modeled by probability distributions. This paper presents a novel Mathematical Programming-Dynamic Programming (MP-DP) clustering method developed by the authors, applied to datasets characterized by exponential, right-triangular, and uniform distributions. The MP-DP technique optimizes cluster partitions by leveraging the probability distributions inherent in the data. We conducted a comparative evaluation to assess the performance of MP-DP against four established clustering methodologies: K-Means, Fuzzy C-Means, expectation-maximization, and Genie++ hierarchical clustering. Results from extensive simulations and real-world datasets consistently demonstrate the superior efficacy of MP-DP in achieving optimal clustering outcomes. Specifically, MP-DP excels in handling diverse data distributions and effectively mitigating the effects of noise and uncertainty, thereby enhancing clustering accuracy and reliability. This study highlights the significant advancement offered by MP-DP in clustering research. It underscores the method’s potential for applications across various domains, such as healthcare, environmental monitoring, and manufacturing, where robust and efficient data clustering is essential for insightful data analysis and decision-making.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Ms Shalni Sanjana
Date Deposited:	31 Mar 2025 21:23
Last Modified:	31 Mar 2025 21:23
URI:	https://repository.usp.ac.fj/id/eprint/14885

Actions (login required)

View Item