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 …

The generation of collective preference assessments occupies a critical position in deriving
accurate and reliable alternative rankings in the context of large-scale group decision …

Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …

In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …

This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

Longitudinal studies are essential tools in medical research. In these studies, variables are
not restricted to single measurements but can be seen as variable-trajectories, either single …