Clustering in data mining is a supreme step toward organizing data into some meaningful
patterns. It plays an extremely crucial role in the entire KDD process, and also as …

The real-world dataset exhibits diversity, incorporating instances with complex shapes and
significant differences in density hierarchy, potentially disrupted by noise. However, most …

Hierarchical clustering has been extensively used in practice, where clusters can be
assigned and analyzed simultaneously, especially when estimating the number of clusters is …

Abstract Highly Improving the Accuracy of Clustering (HIAC) algorithm is designed to
enhance clustering accuracy by introducing a gravitational force between data objects …

Clustering is a data analysis method for extracting knowledge by discovering groups of data
called clusters. Density-based clustering methods have proven to be effective for arbitrary …

There are many different tasks where it is important to find hidden groups in the data that are
much easier to interpret than individual observations. This can be done using data …

Clustering ensemble is an open proposition that aims to solve the limitation of a single
cluster algorithm on the diversity of data structures in data partitions. It obtains a consensus …

Density-based clustering algorithms have become a popular research topic in recent years.
However, most these algorithms have difficulties identifying all clusters with greatly varying …

In the fourth industrial revolution era of today, individuals encounter an immense volume of
information daily. The digital world is rich in data like IoT, social media, healthcare, business …

The clustering method divides a dataset into groups with similar data using similarity metrics.
However, discovering clusters in different densities, shapes and distinct sizes is still a …