K-Means and Hierarchical based Clustering in Suicide Analysis
R Sujatha1, S. Sree Dharinya2, E P Ephzibah3, R Kiruba Thangam4
1R. Sujatha, Department of Information Technology & Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2S. Sree Dharinya, Department of Information Technology & Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3E. P. Ephzibah, Department of Information Technology & Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
4R. Kiruba Thanga, Department of Information Technology & Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 18 February 2019 | Revised Manuscript received on 27 February 2019 | Manuscript published on 28 February 2019 | PP: 405-409 | Volume-8 Issue-3, February 2019 | Retrieval Number: C5962028319/19©BEIESP
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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: Machine learning is the intriguing area of research that spreads across all domains helping in providing quality decisions. Demographic have more influence in social happenings along with various personal and social factors. Suicide analysis is one such issue to be handled with great concern that will provide precautionary based on situations. Suicide prediction can be carried on using data mining that can be used to predict the suicide earlier so that it can be prevented. Suicide is an action resulting in death performed by themselves. Common factors that influence the rate of suicides are cause, method of suicide, year, gender, educational qualification, social status. For this clustering technique in datamining that falls under unsupervised provides great platform. Silhouette score is used for mapping the number of cluster to get the good clustering. Various plots like box plot, scatter plot and so on helps to provide greater insight. Based on analysis the required remedial could be arrived.
Keywords: Clustering, Suicide, Gender, Age, Education
Scope of the Article: Education Systems