K-means plays a vital role in data mining and is the simplest and most widely used algorithm
under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its …

The primary purpose of this paper is to provide an in-depth analysis of different platforms
available for performing big data analytics. This paper surveys different hardware platforms …

Amount of data is getting bigger in every moment and this data comes from everywhere;
social media, sensors, search engines, GPS signals, transaction records, satellites, financial …

An improved version of K‐means clustering algorithm that can be applied to big data
through lower processing loads with acceptable precision rates is presented here. In this …

Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

In modern scientific research, data analyses are often used as a popular tool across
computer science, communication science, and biological science. Clustering plays a …

Data has become the lifeblood of today's knowledge-driven economy and society. Big data
clustering aims to summarize, segment, and group large volumes and varieties of data that …

Clustering is an essential data mining and tool for analyzing big data. There are difficulties
for applying clustering techniques to big data duo to new challenges that are raised with big …

Big data analysis requires the presence of large computing powers, which is not always
feasible. And so, it became necessary to develop new clustering algorithms capable of such …

In this paper we propose a criterion to balance the processing time and the solution quality
of k-means cluster algorithms when applied to instances where the number n of objects is …