Statistically improving k-means clustering performance

Name: Statistically improving k-means clustering performance
Author: Zaval, Mounes, Ihsanoglu, Abdullah

İsim	Statistically improving k-means clustering performance
Yazar	Zaval, Mounes, Ihsanoglu, Abdullah
Basım Tarihi:	2024-01-01
Basım Yeri	- IEEE
Konu	K-means plus, K-means, Unsupervised learning
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-8-3503-8897-8
Kayıt Numarası	054fdd9b-3715-4781-aa9e-876666a0e116
Lokasyon	Computer Science, Artificial Intelligence and Data Engineering
Tarih	2024-01-01
Örnek Metin	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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Statistically improving k-means clustering performance

Yazar Zaval, Mounes, Ihsanoglu, Abdullah

Basım Tarihi 2024-01-01

Basım Yeri - IEEE

Konu K-means plus, K-means, Unsupervised learning

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-8-3503-8897-8

Kayıt Numarası 054fdd9b-3715-4781-aa9e-876666a0e116

Lokasyon Computer Science, Artificial Intelligence and Data Engineering

Tarih 2024-01-01

Örnek Metin 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