摘要
由于时间序列数据具有高维度、动态性等特点,这就导致传统的数据挖掘技术很难有效的对其进行处理,为此,提出了一种基于多维时间序列形态特征的相似性动态聚类算法(similarity dynamical clustering algorithm based on multidimensional shape features for time series,SDCTS).首先,提取多维时间序列的特征点以实现降维,然后,根据多维时间序列的斜率、长度和幅值变化的形态特征定义了一种新的时间序列相似性度量标准,进而提出无需人为给定聚类个数的多维时间序列动态聚类算法.实验结果表明,与其他算法相比,此算法对时间序列具有良好的聚类效果.
Traditional data mining methods are difficult to deal with the high dimensionality and dynamics characteristic of the time series.Therefore,in this study,a similarity dynamical clustering algorithm based on multidimensional shape features for time series(SDCTS) was proposed.First,the feature points of multidimensional time series are extracted to realize dimensionality reduction.Second,a new similarity measure criterion is defined with the shape features(slope,length,and amplitude) of the obtained multidimensional time series,and thus a dynamical clustering algorithm of multidimensional time series is proposed without predefining clustering numbers.The experimental results demonstrate that the SDCTS algorithm improves the clustering accuracy for time series compared with other algorithms.
作者
王玲
孟建瑶
徐培培
彭开香
WANG Ling MENG Jian-yao XU Pei-pei PENG Kai-xiang(School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China Key Laboratory of Knowledge Automationfor Industrial Processes ( Ministry of Education) , University of Science and Technology Beijing, Beijing 100083, China)
出处
《工程科学学报》
EI
CSCD
北大核心
2017年第7期1114-1122,共9页
Chinese Journal of Engineering
基金
国家自然科学基金资助项目(61572073)
北京科技大学中央高校基本科研业务费专项资金资助(FRF-BD-16-005A)
北京市重点学科共建资助项目(XK100080537)
关键词
相似性度量
聚类
时间序列
形态特征
降维
similarity measure
clustering
time series
shape features
dimensionality reduction