DocumentCode
3272144
Title
An Algorithm for Time Series Data Mining Based on Clustering
Author
Wu, Shaozhi ; Wu, Yue ; Wang, Ying ; Ye, Yalan
Author_Institution
Sch. of Comput. Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu
Volume
3
fYear
2006
fDate
25-28 June 2006
Firstpage
2155
Lastpage
2158
Abstract
This paper presents a new method for time series data mining. Discrete Fourier transform (DFT) is used to transform the time series data from time domain to frequency domain. By taking the transformed amplitude of power spectrum as the feature samples of the time series data, time series data can be mapped into a frequency domain space. We use OPTICS (ordering points to identify the cluster structure) algorithm to detect clusters in these data. Several simulations are given based on the price histories of California power market
Keywords
data mining; discrete Fourier transforms; frequency-domain analysis; California power market; DFT; OPTICS; clustering; discrete Fourier transform; frequency domain; time domain; time series data mining; Clustering algorithms; Computer science; Data engineering; Data mining; Databases; Discrete Fourier transforms; Fourier transforms; Frequency domain analysis; History; Power markets;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications, Circuits and Systems Proceedings, 2006 International Conference on
Conference_Location
Guilin
Print_ISBN
0-7803-9584-0
Electronic_ISBN
0-7803-9585-9
Type
conf
DOI
10.1109/ICCCAS.2006.284925
Filename
4064331
Link To Document