Clustering of Hydrological Time Series Based on Discrete Wavelet Transform

被引：7
|
作者：
Wang Hong-fa [1 ]
机构：
[1] Zhejiang Water Conservancy & Hydropower Coll, Hangzhou, Zhejiang, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON SOLID STATE DEVICES AND MATERIALS SCIENCE | 2012年 / 25卷
关键词：
discrete wavelet transform; hydrological series; daubechies wavelet; clustering;
D O I：
10.1016/j.phpro.2012.03.336
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Clustering is an important method in hydrological sequence data mining, where dimension deduction is the key efficiency. In this paper, the Mallat algorithm and Daubechies wavelet are used to conduct wavelet transform on hydrological sequences. Through k-level wavelet transform, the hydrological sequences are divided into approximate part A and detailed part B with different time scales. The sequence length at the k-th level is 1/2(k) times of the length of the original sequence. Thus the efficiency of the algorithm is improved. Real tests show that obtained clusters conform to the real situation. (C) 2012 Published by Elsevier B.V. Selection and/or peer-review under responsibility of Garry Lee
引用
收藏
页码：1966 / 1972
页数：7
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