Density-based clustering algorithms have made a large impact on a wide range of
application fields application. As more data are available with rising size and various …

Abstract The advent of Big Data has led to the rapid growth in the usage of parallel
clustering algorithms that work over distributed computing frameworks such as MPI …

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology
and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer …

Data clustering is an important topic in data science in general, but also in user modeling
and recommendation systems. Some clustering algorithms like K-means require the …

The immune system is a complex infrastructure where many cells interact with each other
and perform duties depending on their type and function. When using traditional …

A Shared Nearest Neighbor (SNN) graph is a type of graph construction using shared
nearest neighbor information, which is a secondary similarity measure based on the …

The major strength of hierarchical clustering algorithms is that it allows visual interpretations
of clusters through dendrograms. Users can cut the dendrogram at different levels to get …

In various information systems, application learning algorithms have to act in a dynamic
environment where the acquired data is in data streams. In contrast to static data mining …

Data Extraction is a technique is called as clustering which is used to retrieve data either
from the files or data bases or both. This paper focuses on the performance evaluation …

Clustering is a popular unsupervised data mining technique. It has been applied in various
data mining and big data applications. Efficient clustering algorithms and implementation …