Recently, various sophisticated methods, including machine learning and artificial
intelligence, have been employed to examine health-related data. Medical professionals are …

Image segmentation is a wide research topic; a huge amount of research has been
performed in this context. Image segmentation is a crucial procedure for most object …

Tradition K-means clustering algorithm is easy to fall into local optimum, poor clustering
effect on large capacity data and uneven distribution of clustering centroids. To solve these …

Clustering is the most fundamental technique for data processing. This paper presents a
collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ …

Despite its well-known shortcomings, k-means remains one of the most widely used
approaches to data clustering. Current research continues to tackle its flaws while …

Principal component analysis (PCA) is one of the most successful unsupervised subspace
learning methods and has been used in many practical applications. To deal with the …

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 …

Abstract Motivation Cell–cell interactions (CCIs) play critical roles in many biological
processes such as cellular differentiation, tissue homeostasis, and immune response. With …

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 …

Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid
selection and perform poorly on non-convex dataset. To address these problems, a novel …