A Survey on Unsupervised K-Means Algorithm in Big Data Environment

Al-deen, Fatama Sharf and Ba-Alwi, Fadl Mutaher (2021) A Survey on Unsupervised K-Means Algorithm in Big Data Environment. Asian Journal of Research in Computer Science, 11 (3). pp. 1-8. ISSN 2581-8260

[thumbnail of 214-Article Text-337-1-10-20220914.pdf] Text
214-Article Text-337-1-10-20220914.pdf - Published Version

Download (186kB)

Abstract

Due to the rapid development in information technology, Big Data has become one of its prominent feature that had a great impact on other technologies dealing with data such as machine learning technologies. K-mean is one of the most important machine learning algorithms. The algorithm was first developed as a clustering technology dealing with relational databases. However, the advent of Big Data has highly effected its performance. Therefore, many researchers have proposed several approaches to improve K-mean accuracy in Big Data environment. In this paper, we introduce a literature review about different technologies proposed for k-mean algorithm development in Big Data. We demonstrate a comparison between them according to several criteria, including the proposed algorithm, the database used, Big Data tools, and k-mean applications. This paper helps researchers to see the most important challenges and trends of the k-mean algorithm in the Big Data environment.

Item Type: Article
Subjects: Afro Asian Library > Computer Science
Depositing User: Unnamed user with email support@afroasianlibrary.com
Date Deposited: 24 Jan 2023 07:43
Last Modified: 17 Jun 2024 07:09
URI: http://classical.academiceprints.com/id/eprint/100

Actions (login required)

View Item
View Item