Bibliometric Survey on Incremental Clustering Algorithms

Authors

Archana Chaudhari, Research Scholar, Symbiosis Institute of Technology (SIT) affiliated to Symbiosis International (Deemed University), Pune, India.Follow
Rahul Raghvendra Joshi, Research Scholar and Assistant Professor, Symbiosis Institute of Technology (SIT) affiliated to Symbiosis International (Deemed University), Pune, India.Follow
Preeti Mulay, Ph.D. Guide and Associate Professor, Symbiosis Institute of Technology (SIT) affiliated to Symbiosis International (Deemed University), Pune, India.Follow
Ketan Kotecha, Director, Symbiosis Institute of Technology (SIT) affiliated to Symbiosis International (Deemed University), Pune, India.Follow
Parag Kulkarni, Innovation Strategist, CEO, and Chief Scientist, iknowlation Research Labs Pvt Ltd Pune, IndiaFollow

Date of this Version

Fall 9-16-2019

Abstract

For clustering accuracy, on influx of data, the parameter-free incremental clustering research is essential. The sole purpose of this bibliometric analysis is to understand the reach and utility of incremental clustering algorithms. This paper shows incremental clustering for time series dataset was first explored in 2000 and continued thereafter till date. This Bibliometric analysis is done using Scopus, Google Scholar, Research Gate, and the tools like Gephi, Table2Net, and GPS Visualizer etc. The survey revealed that maximum publications of incremental clustering algorithms are from conference and journals, affiliated to Computer Science, Chinese lead publications followed by India then United States. Convergence optimality is another prominent keyword and less attentiveness towards correlation has observed. For betweenness and friendly measures keywords, after physics and astronomy; engineering is the contributing subject area, minimal contribution of review papers are observed in this art-search. The effectual incremental learning is feasible via parameter-free incremental clustering algorithm, applicable to all domains and hence this study.