… Several K-means clustering algorithm variants have been developed to enhance its … means
clustering algorithm and its variants with a proposed taxonomy for the variants. The algorithm’…

… were collected for different purpose are combines together both logically and illogically to
derive new insights and information both at real time or not can be said as big data processing. …

… to deploy clustering techniques to big data and get the results in a reasonable time. This
study is aimed to review the trend and progress of clustering algorithms to cope with big data …

… In this paper, we have considered several clustering methods which are presently and …
Still there is a huge gap in examining the big data. Clustering algorithms that consume less time …

… have been proposed for improving the performance of the k-means clustering algorithm. We
propose a method for making the algorithm less time consuming, more effective and efficient …

… of clustering algorithms applied in big data. Moreover, the improvement of clustering algorithms
in big data is … time of big data clustering. The last way is to apply parallel and distributed …

… data. In this paper, we have provided the detailed survey of recent clustering algorithms used
for mining large scale of data. … clustering algorithm named DMMStream for real time stream …

… data on the Internet, we have entered the era of big data with an explosion of information [5].
In addition, some big data … The K-Means algorithm began to expose some of its own …

… We then measure the execution time required by each algorithm on an Intel core i7 2.80 GHz
processor machine with 8 Gbytes of RAM. Table 6 records the runtime of the five candidate …

… A curious fact about our data-obsessed era is that little is known about the origin and the
evolution of this vital concept. Indeed, it is common to think of “big data”, “machine learning” and …