A Survey on Unsupervised K-Means Algorithm in Big Data Environment
Fatama Sharf Al-deen *
Department of Computer Science, Sana’a University, Sana’a, Yemen.
Fadl Mutaher Ba-Alwi
Department of Information System, Sana’a University, Sana’a, Yemen.
*Author to whom correspondence should be addressed.
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.
Keywords: K-mean, Big Data, unsupervised learning, clustering