This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

The text document clustering has become one of the foremost research areas in the field of
data mining. The exponential growth of the textual data in today's digital world has bound …

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a
pioneering algorithm of the density-based clustering technique. It provides the ability to …

Clustering is one of the prime topics in data mining. Clustering partitions the data and
classifies the data into meaningful subgroups. Document clustering is a set of the document …

Cybersecurity continues to be an important concern for financial institutions given the
technology's rapid development and increasing adoption of digital services. Effective safety …

The rapid emergence of computer technology has led to the storage of vast amounts of
information in databases. The increasing popularity of electronic data has also created vast …

In high-dimensional spaces, mathematically driven data processing methods have recently
attracted a lot of attention. We consider the situation when information is obtained by …

The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities
and challenges for land cover analysis. Many supervised machine learning methods have …

In recent years, the clustering ensembles emerged as a problem solver for extracting the
data points into clusters in an efficient way. However, still clustering poses a serious issue …

Artificial intelligence has been provided powerful research attributes like data mining and
clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis …