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 …

Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-
means is one of the well-known algorithms for clustering data. The foundation of this …

The recent wave of digitalization is characterized by the widespread deployment of sensors
in many different environments, eg, multi-sensor systems represent a critical enabling …

Task schedule optimization enables to attain high performance in both homogeneous and
heterogeneous computing environments. The primary objective of task scheduling is to …

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 …

Conventional k-means clustering is the widely used partitional method, mainly adapted to
machine learning and pattern recognition problems. This algorithm is highly sensitive to …

Text clustering has been widely utilized with the aim of partitioning specific document
collection into different subsets using homogeneity/heterogeneity criteria. It has also become …

The application of clustering algorithms is expanding due to the rapid growth of data
volumes. Nevertheless, existing algorithms are not always effective because of high …

Numerous works implemented multi-view clustering algorithms in document clustering. A
challenging problem in document clustering is the similarity metric. Existing multi-view …