In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult
task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic …

This paper focuses on connectivity-based data clustering for categorizing similar and
dissimilar data into distinct groups. Although classical clustering algorithms such as K …

Abstract In recent years, Quantum Inspired Metaheuristic algorithms have emerged to be
promising due to their efficiency, robustness and faster computational capability. In this …

A significant amount of interest has been generated in recent years in the convergence of
quantum computing and data mining due to quantum algorithms' potential to revolutionize …

Metaheuristics are widely perceived optimization methods that provide optimal solutions to
an expansive range of computational problems. This paper provides a survey of the …

The paper presents necessity of data pre-processing to process the data set through
machine learning algorithms, and it filters the raw data to make the data as in compatible …

Meta‐heuristics has various optimization models which resolve various critical problems in
an optimal way. The idea of using the principle of quantum computing to solve the problems …

Clustering is a widely used data mining tool that has been paid enormous attention by the
research community. We introduce here a new clustering algorithm which is based on …

Data clustering has found significant applications in various domains like bioinformatics,
medical data, imaging, marketing study and crime analysis. There are several types of data …

Density-based clustering methods such as DBSCAN are known to be robust against outliers
in data; however, they are sensitive to user-specified parameters, the selection of which are …