"Journal of Data Science
Vol. 3 No. 02 (2025): Journal Of Data Science, September 2025

Comparison and Evaluation of Euclidean Distance and Arccosine Distance in Adaptive K-Means Clustering Algorithm for Penguin Species Clustering

Herlina Br Nainggolan (Unknown)
Pandi Barita Nauli Simangungsong (Unknown)

Article Info

Publish Date
28 Aug 2025

Abstract

Clustering is an important method in unsupervised learning for grouping data based on similarity of characteristics. This study aims to cluster penguin species based on weight, height, and wing length attributes using the K-Means algorithm with two distance approaches: Euclidean and Arccosine. The dataset consists of 342 data points after cleaning. Evaluation results show that the Arccosine distance yields a clustering accuracy of 89.6%, higher than the Euclidean distance at 63.09%. This indicates that Arccosine is more optimal for classifying penguin species.

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Journal Info
"Journal of Data Science
Website

Abbrev

visualization

Publisher

SEAN INSTITUTE

Subject

Automotive Engineering Computer Science & IT

Description

The "Journal of Data Science" is a real journal that focuses on the field of data science. It covers a wide range of topics related to data analysis, machine learning, statistics, data mining, and related areas. The journal aims to publish high-quality research papers, reviews, and technical notes ...