一种新的离群点检测算法及其在智能交通系统中的应用

2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference Pub Date : 2014-12-01 DOI:10.1109/ITAIC.2014.7065088

Gao Lin, Liu Xin, Han Feng, Liu Ying

{"title":"一种新的离群点检测算法及其在智能交通系统中的应用","authors":"Gao Lin, Liu Xin, Han Feng, Liu Ying","doi":"10.1109/ITAIC.2014.7065088","DOIUrl":null,"url":null,"abstract":"Outlier detection plays an important role for data analysis in data mining. Aiming at outlier characters of Intelligent Transportation System (ITS) such as few samples, high frequency and large range, a new outlier detection algorithm based on probability theory and fuzzy clustering method (FCM) is proposed. Firstly, the new algorithm judges data variation, and then clusters data using FCM. Finally, the outlier detection result is given through estimating clustering result using probability theory. Detection of practical travel time verifies validity and practicability of the new algorithm.","PeriodicalId":111584,"journal":{"name":"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A new outlier detection algorithm and its application in intelligent transportation system\",\"authors\":\"Gao Lin, Liu Xin, Han Feng, Liu Ying\",\"doi\":\"10.1109/ITAIC.2014.7065088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Outlier detection plays an important role for data analysis in data mining. Aiming at outlier characters of Intelligent Transportation System (ITS) such as few samples, high frequency and large range, a new outlier detection algorithm based on probability theory and fuzzy clustering method (FCM) is proposed. Firstly, the new algorithm judges data variation, and then clusters data using FCM. Finally, the outlier detection result is given through estimating clustering result using probability theory. Detection of practical travel time verifies validity and practicability of the new algorithm.\",\"PeriodicalId\":111584,\"journal\":{\"name\":\"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITAIC.2014.7065088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITAIC.2014.7065088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

摘要

在数据挖掘中，离群点检测在数据分析中起着重要的作用。针对智能交通系统中离群点样本少、频率高、范围大的特点，提出了一种基于概率论和模糊聚类方法的离群点检测算法。该算法首先对数据的变化进行判断，然后利用FCM聚类数据。最后利用概率论对聚类结果进行估计，给出离群点检测结果。实际旅行时间的检测验证了新算法的有效性和实用性。

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

A new outlier detection algorithm and its application in intelligent transportation system

Outlier detection plays an important role for data analysis in data mining. Aiming at outlier characters of Intelligent Transportation System (ITS) such as few samples, high frequency and large range, a new outlier detection algorithm based on probability theory and fuzzy clustering method (FCM) is proposed. Firstly, the new algorithm judges data variation, and then clusters data using FCM. Finally, the outlier detection result is given through estimating clustering result using probability theory. Detection of practical travel time verifies validity and practicability of the new algorithm.

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

2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference

自引率

0.00%

发文量