基于k均值聚类的软件测量数据离群点检测方法

First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007) Pub Date : 1900-01-01 DOI:10.1109/ESEM.2007.49

Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae

{"title":"基于k均值聚类的软件测量数据离群点检测方法","authors":"Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae","doi":"10.1109/ESEM.2007.49","DOIUrl":null,"url":null,"abstract":"The quality of software measurement data affects the accuracy of project manager's decision making using estimation or prediction models and the understanding of real project status. During the software measurement implementation, the outlier which reduces the data quality is collected, however its detection is not easy. To cope with this problem, we propose an approach to outlier detection of software measurement data using the k-means clustering method in this work.","PeriodicalId":124420,"journal":{"name":"First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method\",\"authors\":\"Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae\",\"doi\":\"10.1109/ESEM.2007.49\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The quality of software measurement data affects the accuracy of project manager's decision making using estimation or prediction models and the understanding of real project status. During the software measurement implementation, the outlier which reduces the data quality is collected, however its detection is not easy. To cope with this problem, we propose an approach to outlier detection of software measurement data using the k-means clustering method in this work.\",\"PeriodicalId\":124420,\"journal\":{\"name\":\"First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)\",\"volume\":\"211 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ESEM.2007.49\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESEM.2007.49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

摘要

软件测量数据的质量影响到项目经理使用估算或预测模型进行决策的准确性，以及对实际项目状态的理解。在软件测量过程中，会收集到降低数据质量的异常值，但异常值的检测并不容易。为了解决这一问题，本文提出了一种基于k-means聚类方法的软件测量数据离群值检测方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method

The quality of software measurement data affects the accuracy of project manager's decision making using estimation or prediction models and the understanding of real project status. During the software measurement implementation, the outlier which reduces the data quality is collected, however its detection is not easy. To cope with this problem, we propose an approach to outlier detection of software measurement data using the k-means clustering method in this work.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)

自引率

0.00%

发文量