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
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;
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
DOI :
10.1109/ICCCAS.2006.284925
