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

Author

Gao Lin ; Liu Xin ; Han Feng ; Liu Ying

Author_Institution

Hisense TransTech Co., Ltd., Qingdao, China

fYear

2014

Firstpage

442

Lastpage

445

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.

Keywords

data mining; fuzzy set theory; intelligent transportation systems; pattern clustering; probability; FCM; ITS; data analysis; data mining; fuzzy clustering method; intelligent transportation system; outlier detection algorithm; probability theory; Algorithm design and analysis; Clustering algorithms; Clustering methods; Data analysis; Data mining; Detection algorithms; Feature extraction; FCM; ITS; Outlier detection; travle time;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International

Print_ISBN

978-1-4799-4420-0

Type

conf

DOI

10.1109/ITAIC.2014.7065088

Filename

7065088