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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Review Article

BibPat: Quantum K-means Clustering with Incremental Enhancement

Author(s): Shradha Deshmukh* and Preeti Mulay

Volume 18, Issue 6, 2024

Published on: 09 August, 2023

Article ID: e160623218039 Pages: 16

DOI: 10.2174/1872212118666230616091148

Price: $65

Abstract

One of the main areas of study within the broader paradigm of quantum machine learning is quantum clustering (QC). Considering the potential time and cost savings that solutions to realworld issues employing QC algorithms bring, in comparison to their classical methods, researchers have recently developed a keen interest in QC. With new algorithms and their applications being invented virtually every other day, this is still a highly young and fascinating area of research. Based on the background information provided, this work aims to analyze research and patent databases spanning twelve years (2010 to mid-2022) to identify and understand the publishing and patent trends in the field of QC. This study aims to study the topological analyses, important study areas, relationships, and collaboration patterns that distinguish traditional and developing research clusters. The graphical representation of the progress of publications and patents over time depends on such rigorous field mapping. This paper presents a comprehensive list of all the sources through the network, bibliometric and patentometric (BibPat) analysis, and future research scope in the QC. The top authors, universities, and research fields were listed after the primary and secondary keywords connected to the quantum K-means clustering algorithm in the analysis design. Reviewing the articles and then delving into the specifics of the patents will help us evaluate the total body of work on the quantum K-means clustering technique. Using the thorough BibPat tools and numerous research and patent databases like Scopus, IEEE, ACM, Google Scholar, Lens, Google Patents, and Espacenet, the analysis design displays the patents and journal papers that have been published. Additionally, it is crucial for later research because it aids in the identification of areas for current research interests and possible avenues for future study. QC offers various studies in disciplines from computer science to psychology. The Ministry of Education, China, produced most publications. Since 2014, the trend has been up, and experts continue studying the issue. The BibPat analysis shows that the Chinese National Natural Science Foundation has facilitated funding for cutting-edge research. In order to open the door for future research and investigation on the substantial amount of unstructured real-time data, the report concluded by proposing an incremental QC approach.

Graphical Abstract

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