Adaptive Incremental Learning Based Fuzzy Clustering of Time Series

WANG Ling; XU Pei-pei

doi:10.3969/j.issn.0372-2112.2019.05.002

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Adaptive Incremental Learning Based Fuzzy Clustering of Time Series

更新时间：2025-07-16

- Adaptive Incremental Learning Based Fuzzy Clustering of Time Series
- Acta Electronica Sinica Vol. 47, Issue 5, Pages: 983-991(2019)
- 作者机构：
  
  1. 北京科技大学自动化学院,北京,100083
  2. 北京科技大学自动化学院工业过程知识自动化教育部重点实验室,北京,100083
  3. 北京科技大学自动化学院,北京,100083
  4. 北京科技大学自动化学院工业过程知识自动化教育部重点实验室,北京,100083
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61572073);Fundamental Research Funds for the Central Universities of University of Science and Technology Beijing (No.FRF-BD-17-002A);Key Discipline Co-construction Project of Beijing Municipality (No.XK100080537)
- DOI：10.3969/j.issn.0372-2112.2019.05.002
  CLC： TP273
- Published Online：25 May 2019，
  
  Published：2019
- 稿件说明：
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WANG Ling, XU Pei-pei. Adaptive Incremental Learning Based Fuzzy Clustering of Time Series[J]. Acta Electronica Sinica, 2019, 47(5): 983-991.
DOI：

WANG Ling, XU Pei-pei. Adaptive Incremental Learning Based Fuzzy Clustering of Time Series[J]. Acta Electronica Sinica, 2019, 47(5): 983-991. DOI： 10.3969/j.issn.0372-2112.2019.05.002.

摘要

针对现存可用于时间序列的增量式模糊聚类算法往往需要设置多个控制参数的问题，本文提出了一种基于自适应增量学习的时间序列模糊聚类算法.该算法首先继承上一次聚类得到的簇结构信息以初始化当前聚类进程，然后在无需设置参数的情况下自适应地搜索当前数据块中的离群样本，并自动从离群样本创建新簇，最后检查空簇识别标识确定是否需要移除部分簇以保证后续聚类过程的效率.实验结果表明所提算法对等长和不等长时间序列均具有良好的聚类准确性及运行效率.

Abstract

Existing incremental fuzzy clustering algorithms which can be used for time series often require setting multiple control parameters.To solve this problem

a fuzzy clustering algorithm of time series based on adaptive incremental learning is proposed.First

the cluster structure information obtained by the previous clustering process is inherited to initialize the current clustering process.Then

the outliers in current data block are adaptively searched without parameters

and new clusters are automatically created from the outliers.Finally

an empty cluster flag is checked to determine if some clusters need to be removed to ensure the favorable efficiency of subsequent clustering.The experimental results show that the proposed algorithm has good clustering accuracy and efficiency for both equal-length and unequal-length time series.

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