Optimizing Clustering Algorithms for Anti-Microbial Evaluation Data: A Majority Score-Based Evaluation of K-Means, Gaussian Mixture Model, and Multivariate T-Distribution Mixtures-Reference-Cited by-同舟云学术

Optimizing Clustering Algorithms for Anti-Microbial Evaluation Data: A Majority Score-Based Evaluation of K-Means, Gaussian Mixture Model, and Multivariate T-Distribution Mixtures

Published:2023 Issue: Volume:11 Page:79793-79800
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mahmood Hira¹^ORCID,Mehmood Tahir¹^ORCID,Al-Essa Laila A.²^ORCID

Affiliation:

1. Department of Mathematics, School of Natural Sciences, National University of Sciences and Technology (NUST), Islamabad, Pakistan

2. Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, through the Princess Nourah bint Abdulrahman University Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10158702.pdf?arnumber=10158702

Reference23 articles.

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2. A comparison of internal and external cluster validation indexes;rendón;Proc Amer Conf,2011

3. Novel benzimidazole–oxadiazole hybrid molecules as promising antimicrobial agents

4. Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model

5. A Clustering Method Based on K-Means Algorithm

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