An Algorithm for Time Series Data Mining Based on Clustering

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.

作者: Shaozhi Wu Yue Wu Ying Wang Yalan Ye

作者单位: School of Computer Science and Engineering, University of Electronic Science and Technology of China Electric Engineering Department Chongqing University Chongqing, 400044, China

会议类型: 国际会议

会议名称: 2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

会议地点: 广西桂林

会议语种:英文

页码: 2155-2158

在线出版日期: 2006-06-25（万方平台首次上网日期，不代表论文的发表时间）