An algorithm for time series data mining based on clustering

被引：2
|
作者：
Wu, Shaozhi [1 ]
Wu, Yue [1 ]
Wang, Ying [2 ]
Ye, Yalan [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China
[2] Chongqing Univ, Dept Elect Engn, Chongqing 400044, Peoples R China
来源：
2006 INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CIRCUITS AND SYSTEMS PROCEEDINGS, VOLS 1-4: VOL 1: SIGNAL PROCESSING | 2006年
关键词：
D O I：
10.1109/ICCCAS.2006.284925
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：2155 / +
页数：2
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