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 amount of data extracted from production processes has increased exponentially due to
the proliferation of sensing technologies. When processed and analyzed, data can bring out …

Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the
territory. Instead, it focuses on hierarchical agglomerative clustering, k‐means clustering …

Conventional planning of maintenance and renewal work for railway track is based on
heuristics and simple scheduling. The railway industry is now collecting a large amount of …

The most well-known and ubiquitous clustering problem encountered in nearly every branch
of science is undoubtedly k-means: given a set of data points and a parameter k, select k …

K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Nowadays, modern vehicles are becoming even more connected, intelligent, and smart. A
modern vehicle encloses several cyber–physical systems, such as actuators and sensors …

The selection of a proper distance function is crucial for analyzing the data efficiently. To find
an appropriate distance for clustering algorithm is an unsolved problem as of now. The …

Frequency response analysis (FRA) is considered as the most popular and reliable method
to detect mechanical deformations within power transformers. Despite this popularity …

The k-means algorithm is a widely used method that starts with an initial partitioning of the
data and then iteratively converges towards the local solution by reducing the Sum of …