Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method
Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method
复制标题
使用肘部和轮廓方法确定 K 均值聚类中的 K 值时距离度量的影响
DOI：
10.2991/aisr.k.200424.051
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Liniyanti D. Oswari
中科院分区：
文献类型：
--
作者：
D. Saputra;D. Saputra;Liniyanti D. Oswari
聚类是数据挖掘的主要任务之一。对数据进行分组和聚类非常有用。数据聚类的方法有基于分区的、基于层次的和基于密度的。基于分区的聚类是一种对具有非重叠子集的数据进行聚类的方法。最流行的基于分区的聚类算法之一是K-means。K-means算法是一种将数据聚类到K类中，并根据它们到其质心的距离进行聚类的算法。由于这一点，在使用K-means之前必须确定的几个因素是K的值。确定K的值是一个大问题，因为没有通用的方法来找到K的值。确定K值的两种流行方法是使用肘部和轮廓法。该方法是基于图形的。但在使用此方法之前，另一个重要因素需要确定，即将使用的度量距离。本文将展示三种距离度量曼哈顿、欧几里得和闵可夫斯基在用肘形和侧影法求K值时的作用。基于这项研究，所使用的距离矩阵的选择对使用肘部和轮廓确定K均值中的K值几乎没有影响。曼哈顿距离手肘和轮廓图中具有最大的变化。肘形法使用困难，有时无法根据其图形定义K-均值中的K值。
Clustering is one of the main task in datamining. It is useful to group and cluster the data. There are a few ways to cluster the data such as partitional-based, hierarchical-based and density based. Partitional-based clustering is a way to cluster data with non-overlapping subsets. One of the most popular partitional-based clustering algorithm is K-means. K-means is an algorithm to cluster data in to K cluster and based their distance to its centroid. Due to the pational, a few factors that must be determined before using K-means is the value of K. Determining the value of K is a big problem because there is no universal way to find the value of K. Two popular ways to determine the value of K is using elbow and silhouette method. This method is graph based. But before using this method another factor is important to determine and that is the metrics distance that will be used. This paper will show the effect of three distance metric Manhattan, Euclidian and Minkowski in finding the value of K using elbow and silhouette method. Based on this study the choice of distance matrix used has little impact in determining the value of K in K-means using elbow and silhouette. Manhattan distance has the most variant in the elbow and silhouette graph. Elbow method is difficult to use and sometimes it is unable to define the value of K in K-means based on its graph.