K-Means Clustering for Smart Cities: An Empirical Study in Indian Context
Biswajit Biswas1, Sayantan Ghosh2
1Biswajit Biswas, Department of Business Administration, University of Kalyani, Kolkata, West Bengal, India.
2Sayantan Ghosh, Performance-io, Kolkata, West Bengal, India.
Manuscript received on 04 July 2024 | Revised Manuscript received on 09 July 2024 | Manuscript Accepted on 15 July 2024 | Manuscript published on 30 July 2024 | PP: 20-26 | Volume-14 Issue-3, July 2024 | Retrieval Number: 100.1/ijsce.B811413020724 | DOI: 10.35940/ijsce.B8114.14030724
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cities are the concourse of people where they live, meet with new people in their daily life, exchange their thoughts and ideas with close one, and earn better livelihoods so that everyone can access proper education, better health facilities, and other essential services and live in a hazards free peace complete environment. The Idea behind smart cities is to provide datadriven solutions to social problems, especially in a densely populated area of the country. The population continues to grow, and urbanisation is causing an increase in urban areas. Advanced data-supported solutions, aided by technology, can be the best solution for simple Governance. Smart City is a datadriven solution for managing people and city resources effectively, enabling efficient monitoring and management of resources. It helps to make informed social and economic decisions. The authors studied and identified the present problem, which is a lack of basic infrastructure for community service, particularly in India’s small-to-medium cities, as well as the existing bright city development plan.
Keywords: Smart City, Dendrogram, K-Means Clustering, Discriminant Analysis.
Scope of the Article: Computer Science and Its Applications