Density-based clustering techniques identify arbitrary shaped clusters in the presence of
outliers by capturing the intrinsic distribution of data and separating high and low-density …

Spectral clustering is a popular unsupervised learning technique used for exploratory
analysis of complex datasets. In spectral clustering, the efficient construction of a sparse …

In molecular biology, gene expression analysis is one of the important research areas which
deals with identifying the genes having similar functionality known as co-expressed genes …

Today, depression is a psychological condition that affects many individuals globally and, if
untreated, can negatively impact one's emotions and lifestyle quality. Machine learning (ML) …

Cluster analysis is a crucial data mining technology widely used in image segmentation,
language processing, and pattern recognition. Most existing clustering algorithms cannot …

Clustering has been widely applied in interpreting the underlying patterns in microarray
gene expression profiles, and many clustering algorithms have been devised for the same …

Abstract Minimum Spanning Tree (MST) based clustering algorithms have been employed
successfully to detect clusters of heterogeneous nature. Given a dataset of n random points …

This paper presents two different versions of a new internal index for clustering validation
using graphs. These graphs capture the structural characteristics of each cluster. In this way …

Tourism agglomeration area division plays an increasingly important role in government's
policy making on planning and development of tourism industry nowadays. With the …

Density based clustering techniques discover the intrinsic clusters by separating the regions
present in the dataset as high-and low-density regions based on their neighborhood …