基于多維時間序列形態特征的相似性動態聚類算法

王玲; 孟建瑤; 徐培培; 彭開香

doi:10.13374/j.issn2095-9389.2017.07.019

基于多維時間序列形態特征的相似性動態聚類算法

doi: 10.13374/j.issn2095-9389.2017.07.019

王玲^1,2, ,,
孟建瑤^1,2,
徐培培^1,2,
彭開香^1,2

1) 北京科技大學自動化學院, 北京 100083
2) 北京科技大學工業過程知識自動化教育部重點實驗室, 北京 100083

基金項目:

國家自然科學基金資助項目(61572073)

北京市重點學科共建資助項目(XK100080537)

北京科技大學中央高校基本科研業務費專項資金資助(FRF-BD-16-005A)

詳細信息

中圖分類號: TP311
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- 被引次數: 0
出版歷程
- 收稿日期: 2017-01-03

Similarity dynamical clustering algorithm based on multidimensional shape features for time series

WANG Ling^{1,2
, ,},
MENG Jian-yao^1,2,
XU Pei-pei^1,2,
PENG Kai-xiang^1,2

1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2) Key Laboratory of Knowledge Automationfor Industrial Processes(Ministry of Education), University of Science and Technology Beijing, Beijing 100083, China

摘要

摘要: 由于時間序列數據具有高維度、動態性等特點,這就導致傳統的數據挖掘技術很難有效的對其進行處理,為此,提出了一種基于多維時間序列形態特征的相似性動態聚類算法(similarity dynamical clustering algorithm based on multidimensionalshape features for time series,SDCTS).首先,提取多維時間序列的特征點以實現降維,然后,根據多維時間序列的斜率、長度和幅值變化的形態特征定義了一種新的時間序列相似性度量標準,進而提出無需人為給定聚類個數的多維時間序列動態聚類算法.實驗結果表明,與其他算法相比,此算法對時間序列具有良好的聚類效果.
- 相似性度量 /
- 聚類 /
- 時間序列 /
- 形態特征 /
- 降維
Abstract: 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.
- similarity measure /
- clustering /
- time series /
- shape features /
- dimensionality reduction

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參考文獻(5)

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