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

机译：一种新的异常检测算法及其在智能交通系统中的应用

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摘要

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.

机译：异常值检测对数据挖掘中的数据分析扮演重要作用。针对智能运输系统的异常特征（其），如少数样品，高频和大的范围，提出了一种基于概率理论和模糊聚类方法（FCM）的新的异口检测算法。首先，新算法判断数据变化，然后使用FCM群集数据。最后，通过使用概率理论估计聚类结果来给出异常检测结果。检测实际旅行时间验证新算法的有效性和实用性。

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《IEEE Joint International Information Technology and Artificial Intelligence Conference》|2014年||共4页
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作者
Gao Lin; Liu Xin; Han Feng; Liu Ying;
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正文语种
中图分类人工智能理论;
关键词
FCM; ITS; Outlier detection; travle time;

机译：FCM;它;异常检测;旅行时间;

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4. A new outlier detection algorithm and its application in intelligent transportation system [C] . Gao Lin, Liu Xin, Han Feng, 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference . 2014

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

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