{"title":"提高大数据集K-Means算法的效率","authors":"C. Swapna, V. Kumar, J. Murthy","doi":"10.4018/IJRSDA.2016040101","DOIUrl":null,"url":null,"abstract":"Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Improving Efficiency of K-Means Algorithm for Large Datasets\",\"authors\":\"C. Swapna, V. Kumar, J. Murthy\",\"doi\":\"10.4018/IJRSDA.2016040101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.\",\"PeriodicalId\":152357,\"journal\":{\"name\":\"Int. J. Rough Sets Data Anal.\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Rough Sets Data Anal.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJRSDA.2016040101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Rough Sets Data Anal.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJRSDA.2016040101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving Efficiency of K-Means Algorithm for Large Datasets
Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.