Genetic Algorithm Based Parallel K-Means Data Clustering Algorithm Using MapReduce Programming Paradigm on Hadoop Environment (GAPKCA)

Alshammari, Sayer; Zolkepli, Maslina; Abdullah, Rusli

doi:10.1007/978-3-030-36056-6_10

Cited by 5 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The evaluation showed an improvement in data scalability. Moreover, the authors in [8] have proposed a new algorithm. The objective of the recommended algorithm was to unite the search process of GA to generate new data clusters.…”

Section: Speed Improvement Of K-means Algorithmmentioning

confidence: 99%

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

Al-deen

Ba-Alwi

2021

AJRCoS

View full text Add to dashboard Cite

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.

show abstract

Section: Speed Improvement Of K-means Algorithmmentioning

confidence: 99%