{"title":"云中的K-means聚类——一个Mahout测试","authors":"R. Esteves, Rui Pais, Chunming Rong","doi":"10.1109/WAINA.2011.136","DOIUrl":null,"url":null,"abstract":"The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising technology to solve data intensive problems which were not trivial in the past. In this work we studied the performance of Mahout using a large data set. The tests were running on Amazon EC2 instances and allowed to compare the gain in runtime when running on a multi node cluster. This paper presents some results of ongoing research.","PeriodicalId":355789,"journal":{"name":"2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"89","resultStr":"{\"title\":\"K-means Clustering in the Cloud -- A Mahout Test\",\"authors\":\"R. Esteves, Rui Pais, Chunming Rong\",\"doi\":\"10.1109/WAINA.2011.136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising technology to solve data intensive problems which were not trivial in the past. In this work we studied the performance of Mahout using a large data set. The tests were running on Amazon EC2 instances and allowed to compare the gain in runtime when running on a multi node cluster. This paper presents some results of ongoing research.\",\"PeriodicalId\":355789,\"journal\":{\"name\":\"2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"89\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WAINA.2011.136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WAINA.2011.136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising technology to solve data intensive problems which were not trivial in the past. In this work we studied the performance of Mahout using a large data set. The tests were running on Amazon EC2 instances and allowed to compare the gain in runtime when running on a multi node cluster. This paper presents some results of ongoing research.
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