Andleeb Aslam, Usman Qamar, Reda Ayesha Khan, Pakizah Saqib
{"title":"基于初始质心点的k -均值法改进","authors":"Andleeb Aslam, Usman Qamar, Reda Ayesha Khan, Pakizah Saqib","doi":"10.23919/ICACT48636.2020.9061522","DOIUrl":null,"url":null,"abstract":"The paper is concerned with Improving k-Mean Algorithm in terms of accuracy by selecting the best initial seed points based on the provided k value. This paper presents two modified k-mean method for the selection of initial centroid points. In the first method based on the calculated k value with the help of elbow method, the original sorted data based on distances calculated using Euclidean distance method is divided into k equal partitions. And the mean of each partition is considered as initial centroid points. And in the second method the number of k is chosen randomly and the mean of each partition is considered as initial centroid points. We compared within cluster distance and number of iterations. Modified k-mean methods are better than original k-mean method as the distance within the clusters are less in modified k-mean than the original k-mean and the accuracy is also better.","PeriodicalId":296763,"journal":{"name":"2020 22nd International Conference on Advanced Communication Technology (ICACT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Improving K-Mean Method by Finding Initial Centroid Points\",\"authors\":\"Andleeb Aslam, Usman Qamar, Reda Ayesha Khan, Pakizah Saqib\",\"doi\":\"10.23919/ICACT48636.2020.9061522\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper is concerned with Improving k-Mean Algorithm in terms of accuracy by selecting the best initial seed points based on the provided k value. This paper presents two modified k-mean method for the selection of initial centroid points. In the first method based on the calculated k value with the help of elbow method, the original sorted data based on distances calculated using Euclidean distance method is divided into k equal partitions. And the mean of each partition is considered as initial centroid points. And in the second method the number of k is chosen randomly and the mean of each partition is considered as initial centroid points. We compared within cluster distance and number of iterations. Modified k-mean methods are better than original k-mean method as the distance within the clusters are less in modified k-mean than the original k-mean and the accuracy is also better.\",\"PeriodicalId\":296763,\"journal\":{\"name\":\"2020 22nd International Conference on Advanced Communication Technology (ICACT)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 22nd International Conference on Advanced Communication Technology (ICACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ICACT48636.2020.9061522\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 22nd International Conference on Advanced Communication Technology (ICACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICACT48636.2020.9061522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving K-Mean Method by Finding Initial Centroid Points
The paper is concerned with Improving k-Mean Algorithm in terms of accuracy by selecting the best initial seed points based on the provided k value. This paper presents two modified k-mean method for the selection of initial centroid points. In the first method based on the calculated k value with the help of elbow method, the original sorted data based on distances calculated using Euclidean distance method is divided into k equal partitions. And the mean of each partition is considered as initial centroid points. And in the second method the number of k is chosen randomly and the mean of each partition is considered as initial centroid points. We compared within cluster distance and number of iterations. Modified k-mean methods are better than original k-mean method as the distance within the clusters are less in modified k-mean than the original k-mean and the accuracy is also better.