Title :
Discrete Fourier Transformation for Seasonal-Factor Pattern Classification and Assignment
Author :
Luou Shen ; Chenxi Lu ; Fang Zhao ; Weiming Liu
Author_Institution :
Dept. of Transp. Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
This paper introduces a data mining method to investigate the relationship between seasonal factors (SFs) and land use characteristics for urban areas in Florida through discrete Fourier transformation (DFT). First, DFT is applied to discover seasonal variation patterns, and two typical patterns were identified. Second, linear regression is used to determine influential variables, and a weighted similarity method derived from the amplitude of each DFT wave is applied for the SF assignment. The results obtained by DFT demonstrate promising assignment accuracy with a mean absolute percentage error of 4.27% for all data and 3.96% for the low seasonal household percentage subclass.
Keywords :
data mining; discrete Fourier transforms; pattern classification; regression analysis; DFT; data mining; discrete Fourier transformation; land use characteristic; linear regression; pattern assignment; seasonal-factor pattern classification; urban area; weighted similarity method; Accuracy; Discrete Fourier transforms; Educational institutions; Estimation; Linear regression; Roads; Urban areas; Discrete Fourier transformation (DFT); land use; linear regression; pattern classification; seasonal factor (SF);
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2012.2219581
