基于2020年人民福利的南苏拉威西省县市分组k -原型算法

Muhammad Refaldy, S. Annas, Z. Rais
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引用次数: 1

摘要

聚类用于分析机器学习、数据挖掘、模式工程、图像分析和生物信息学中的数据。为了使用聚类过程产生数据分析所需的信息,这是因为数据具有很大的种类和数量。研究人员将使用K-Prototype方法,该方法将成为处理混合类型数据的一种高效算法。K-Prototype算法在寻找最佳簇数方面存在问题。因此,在本文中,研究人员将通过K-Prototype方法寻找最佳簇数来进行研究。有很多方法可以确定这一点,其中之一是肘部法。从若干簇的SSE (Sum Square Error)图可以看出该方法的确定。聚类结果根据k值下降幅度最大形成2个最优聚类。结果表明,集群1是一个比集群2更具有人民福利特征的集群
本文章由计算机程序翻译,如有差异,请以英文原文为准。
K-Prototype Algorithm in Grouping Regency/City in South Sulawesi Province Based on 2020 People's Welfare
Clustering is something that is used to analyze data in machine learning, data mining, pattern engineering, image analysis, and bioinformatics. To produce the information needed for a data analysis using the clustering process, this is because the data has a large variety and amount. Researchers will use the K-Prototype method where this method becomes an efficient and effective algorithm in processing mixed-type data. The K-Prototype algorithm has problems in finding the best number of clusters. So, in this paper, researchers will conduct research by finding the best number of clusters in the K-Prototype method. There are many ways to determine this, one of which is the Elbow method. The determination of this method is seen from the SSE (Sum Square Error) graph of several number of clusters. The results of the clustering formed 2 clusters which were considered optimal based on the value of k that experienced the greatest decrease. The results showed that Cluster 1 is a cluster that has characteristics of people's welfare which is better than Cluster 2
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