{"title":"Comparative Study of K-Means Clustering Using Iris Data Set for Various Distances","authors":"Adrija Chakraborty, Neetu Faujdar, Akash Punhani, Shipra Saraswat","doi":"10.1109/Confluence47617.2020.9058328","DOIUrl":null,"url":null,"abstract":"K-means clustering is an algorithm, which has been used to cluster the given data into k sets that are mutual exclusive of each other. The K-means algorithm is designed to work with the Euclidean distance but there are many measures to identify the dissimilarity of the dataset. The aim of this paper is to discuss the performance of K-means clustering algorithm on city block, cosine, and correlation distance which are used to get the results and further their performance has been shown in terms of accuracy. For classification, authors have chosen the IRIS data set. K means have claimed 98% accuracy on city block and correlation distance.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Confluence47617.2020.9058328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
K-means clustering is an algorithm, which has been used to cluster the given data into k sets that are mutual exclusive of each other. The K-means algorithm is designed to work with the Euclidean distance but there are many measures to identify the dissimilarity of the dataset. The aim of this paper is to discuss the performance of K-means clustering algorithm on city block, cosine, and correlation distance which are used to get the results and further their performance has been shown in terms of accuracy. For classification, authors have chosen the IRIS data set. K means have claimed 98% accuracy on city block and correlation distance.
k -means聚类是一种算法,它被用来将给定的数据聚类成k个相互排斥的集合。K-means算法是设计用来处理欧几里得距离的,但是有很多方法可以识别数据集的不相似性。本文的目的是讨论K-means聚类算法在城市街区、余弦和相关距离上的性能,并进一步在精度方面展示了它们的性能。对于分类,作者选择了IRIS数据集。K均值在城市街区和相关距离上的准确率达到98%。