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Statistically improving k-means clustering performance

Title	Statistically improving k-means clustering performance
Author	Zaval, Mounes, Ihsanoglu, Abdullah
Publication Date:	2024-01-01
Publication Place	- IEEE
Subject	K-means plus, K-means, Unsupervised learning
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	979-8-3503-8897-8
Record ID	054fdd9b-3715-4781-aa9e-876666a0e116
Library Location	Computer Science, Artificial Intelligence and Data Engineering
Date	2024-01-01
Sample Text	The traditional K-means algorithm is a cornerstone in unsupervised learning, providing a simple yet effective method for data clustering. However, its reliance on random initialization often leads to sub-optimal clustering results. This paper introduces an enhanced version of K-means algorithm, aimed at improving the clustering results. Our proposed methodology depends on identifying clusters that need to be partitioned and clusters that need to be merged through a series of statistical operation and iteratively resolve the problem leading to better clusters. We compare our proposed approach to K-Means and K-means++ algorithms on S-2, California housing prices, and EMNIST datasets showing performance improvements.
DOI	10.1109/SIU61531.2024.10601123

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Özyeğin University

Statistically improving k-means clustering performance

Author Zaval, Mounes, Ihsanoglu, Abdullah

Publication Date 2024-01-01

Publication Place - IEEE

Subject K-means plus, K-means, Unsupervised learning

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 979-8-3503-8897-8

Record ID 054fdd9b-3715-4781-aa9e-876666a0e116

Library Location Computer Science, Artificial Intelligence and Data Engineering

Date 2024-01-01

Sample Text The traditional K-means algorithm is a cornerstone in unsupervised learning, providing a simple yet effective method for data clustering. However, its reliance on random initialization often leads to sub-optimal clustering results. This paper introduces an enhanced version of K-means algorithm, aimed at improving the clustering results. Our proposed methodology depends on identifying clusters that need to be partitioned and clusters that need to be merged through a series of statistical operation and iteratively resolve the problem leading to better clusters. We compare our proposed approach to K-Means and K-means++ algorithms on S-2, California housing prices, and EMNIST datasets showing performance improvements.

DOI 10.1109/SIU61531.2024.10601123