DocumentCode :
1928927
Title :
Similarity Search Over Data Stream using LPC-DTW
Author :
Li, Wei-min ; Li, Feng ; Liu, Jian-wei ; Le, Jia-jin
Author_Institution :
Donghua Univ., Shanghai
Volume :
3
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
1631
Lastpage :
1634
Abstract :
Effective similarity search over data stream is of importance for applications like network monitoring, information retrieval and financial service, etc. Linear predictive coding (LPC) is a tool using the information of a linear predictive model. In this paper, we propose similarity search over data stream based on LPC cepstral coefficients using dynamic time warping (DTW). Compared with traditional approaches, such as similarity search based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT), the proposed method LPC-DTW can use fewer coefficients to capture desired features from data stream for similarity search. In distance metric of similarity search, DTW that replaces the Euclidean distance metric could bring better performance. The relative experiment results demonstrate the proposed method is better than the traditional approaches.
Keywords :
discrete Fourier transforms; discrete wavelet transforms; linear predictive coding; search problems; DFT; DWT; Euclidean distance metric; LPC-DTW; cepstral coefficients; data stream; discrete Fourier transform; discrete wavelet transform; dynamic time warping; linear predictive coding; linear predictive model; similarity search; Cepstral analysis; Data mining; Discrete Fourier transforms; Discrete wavelet transforms; Educational institutions; Euclidean distance; Feature extraction; Information retrieval; Linear predictive coding; Predictive models; DTW; Data stream; LPC; Similarity search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370407
Filename :
4370407
Link To Document :
بازگشت