As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

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 …

Parallel processing has turned into one of the emerging fields of machine learning due to
providing consistent work by performing several tasks simultaneously, enhancing reliability …

In recent years, advances in information technology have led to an increasing number of
devices (or things) being connected to the internet; the resulting data can be used by …

The conventional procedures of clustering algorithms are incapable of overcoming the
difficulty of managing and analyzing the rapid growth of generated data from different …

Fuzzy c-means (FCM) is an effective clustering algorithm, which has been successfully
applied on many real-world applications. Although, FCM and its improvements have …

K-Means es uno de los algoritmos de agrupamiento más utilizados debido a su fácil
implementación e interpretación de sus resultados. El problema de agrupamiento de K …

Fuzzy c-means (FCM) is an effective clustering algorithm, which has been successfully
applied on many real-world applications. Although, FCM and its improvements have …

This paper proposes a method to reduce the computations of the K-Means clustering
algorithm for big data. First, with the PCA algorithm, the dimensions of datasets are reduced …

The development of modern high throughput sequencing techniques has resulted in an
exponential growth in meta-genomic sequence accumulation that could greatly enhance …