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 …

Time series clustering is the act of grou** time series data without recourse to a label.
Algorithms that cluster time series can be classified into two groups: those that employ a time …

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …

This paper presents a novel accelerated exact k-means called as" Ball k-means" by using
the ball to describe each cluster, which focus on reducing the point-centroid distance …

This paper presents a novel accelerated exact-means called as “Ball-means” by using the
ball to describe each cluster, which focus on reducing the point-centroid distance …

With growing deployment of machine learning (ML) models, ML developers are training or re-
training increasingly more deep neural networks (DNNs). They do so to find the most …

Due to simplicity, K-means has become a widely used clustering method. However, its
clustering result is seriously affected by the initial centers and the allocation strategy makes …

Nowadays, data in the real world often comes from multiple sources, but most existing multi-
view-Means perform poorly on linearly non-separable data and require initializing the …

The k-means is the most popular clustering algorithm, but, as it needs too many distance
computations, its speed is dramatically fall down against high-dimensional data. Although …

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means
clustering, k-medoids clustering requires the cluster centers to be actual data points and …