Application of K-Means Algorithm to Mapping Poverty Outline by Province in India
Pushpendra Kumar Verma1, Preety2
1Dr. Pushpendra Kumar Verma, Associate Professor, School of Computer Science and Application, IIMT University, Meerut, UP, India.
2Dr. Preety, Assistant Professor, Faculty of Management Swami Vivekanad subharti University Meerut UP India.
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 1045-1049 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7357038620/2020©BEIESP | DOI: 10.35940/ijrte.F7357.038620
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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: India has a second largest population and seventh largest country in the world, the UN data in 2018 recorded that there were 1,368,681,134 more people scattered throughout the Indian provinces. In addition, India also has a variety of social problems, one of which is poverty. The poverty line number in Indonesia needs to be improved. Data utilization techniques become new information called data mining. One of the most popular data mining methods is clustering using the k-means algorithm. K-means can process data without being notified in advance of the class label. This study will produce three provincial groups according to very low, low and sufficient income figures. Data processing of poverty line numbers in India using the k-means algorithm to get the results of the Davies Bouldin index of 0.271. These results are considered well enough because the closer the results obtained with zeros, the better the data similarity between members of the cluster.
Keywords: Poverty Line, K-means, cluster analysis Data Mining.
Scope of the Article: Data Mining.