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 …

The k-means clustering algorithm is considered one of the most powerful and popular data
mining algorithms in the research community. However, despite its popularity, the algorithm …

Clustering is one of the main methods for getting insight on the underlying nature and
structure of data. The purpose of clustering is organizing a set of data into clusters, such that …

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

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 …

K-means is one of the most simple and popular clustering algorithms, which implemented as
a standard clustering method in most of machine learning researches. The goal of K-means …

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 …

Clustering is the process of grouping the data based on their similar properties. Meanwhile,
it is the categorization of a set of data into similar groups (clusters), and the elements in each …

This paper presents Yinyang K-means, a new algorithm for K-means clustering. By
clustering the centers in the initial stage, and leveraging efficiently maintained lower and …

K-means is a widely used partitional clustering method. While there are considerable
research efforts to characterize the key features of K-means clustering, further investigation …