As a branch of statistics, cluster analysis has been extensively studied and widely used in
many applications. Cluster analysis has recently become a highly active topic in data mining …

Clustering is a widely used unsupervised learning method to group data with similar
characteristics. The performance of the clustering method can be in general evaluated …

Cluster detection is important and widely used in a variety of applications, including public
health, public safety, transportation, and so on. Given a collection of data points, we aim to …

Clustering is fundamental for using big data. However, AP (affinity propagation) is not good
at non-convex datasets, and the input parameter has a marked impact on DBSCAN (density …

Density based Spatial clustering of application with noise DBSCAN is a well-known
clustering algorithm that can find clusters with arbitrary shape and handle noisy points …

Clustering analysis is a fundamental technique in machine learning, which is also widely
used in information granulation. Multiple clustering systems granulate a data set into …

DBSCAN has been widely used in density-based clustering algorithms. However, with the
increasing demand for Multi-density clustering, previous traditional DSBCAN can not have …

During the last years, constrained clustering has emerged as an interesting direction in
machine learning research. With constrained clustering, the quality of results can be …

Issues relating to clustering today are computational techniques, optimization, and
performance of clustering algorithms. In this research, a metaheuristic grouping method of …