西爪哇地区新冠肺炎病例数据聚类的k - mediids算法实现

Ririn Restu Aria
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引用次数: 0

摘要

自2020年3月以来,Covid - 19大流行袭击了印度尼西亚近15个月。该病毒已蔓延到印度尼西亚所有省份。为减少或防止冠状病毒的传播作出了各种努力,包括在包括西爪哇省在内的各个地区实施公共卫生服务计划。在本研究中,本研究的目的是对西爪哇根据2021年5月20日发生的地区/城市每天重述的Covid - 19病例数据进行聚类。聚类过程采用K-medoids算法,根据使用的变量确定3个聚类,分别是:丢弃的密切接触、丢弃的疑似点、可能完成的疑似点、可能死亡的疑似点、完全阳性的疑似点、正恢复的疑似点、正死亡的疑似点。在数据处理方面,利用K-medoids算法和Rapidminer应用程序中的阶段进行计算分析,其中高聚类映射为6个区/市,中等聚类映射为19个区/市,低聚类映射为2个区/市。预计分析结果将提供有关西爪哇省集群分布和地图绘制的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of the K-Medoids Algorithm for Data Clustering of Covid 19 Cases in West Java
The Covid 19 pandemic has hit Indonesia for almost 15 months since March 2020. The virus has spread to all provinces in Indonesia. Various efforts were made to be able to reduce or prevent the spread of the coronavirus, including the implementation of the PSBB in various areas including in West Java province. In this study, the objective of this research is to cluster the data on cases of Covid 19 in West Java which are recapitulated daily based on districts/cities that occurred on May 20, 2021. For the clustering process, the K-medoids algorithm is used which determines 3 clusters based on the variables used, namely discarded close contact, suspects discarded, probable completed, probable died, totally positive, positive recovered, and positive died. For data processing, a calculation analysis was carried out using the stages in the K-medoids algorithm and the Rapidminer application with high cluster mapping of 6 districts/cities, medium clusters there were 19 districts/cities, while low clusters had 2 districts/cities. The results of the analysis are expected to provide information about the distribution and mapping of clusters in West Java province.  
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