统计与决策2026，Vol.42Issue(5)：41-47,7.DOI:10.13546/j.cnki.tjyjc.2026.05.007
数据驱动下基于时间序列云模型的特征选择聚类算法研究
Data-driven Feature Selection Clustering Algorithm Based on Time Series Cloud Model
摘要
Abstract
Due to the multivariate and high-dimensional nature of time series data,the difficulty of extracting important fea-tures increases,thereby reducing the accuracy and precision of high-dimensional data clustering.Therefore,in view of the nonlin-ear and high-dimensional redundancy characteristics of multivariate time series data,this paper first proposes an effective and scalable hybrid feature selection clustering algorithm based on time series cloud models on the basis of the research on traditional feature selection algorithms,cloud models,and complex time series.Then,for the extracted multi-dimensional time series data features,a method combining cloud model time similarity and multi-objective particle swarm optimization algorithm is applied for feature screening and feature optimization to obtain more high-quality features,thereby effectively improving the clustering accu-racy of the hybrid algorithm.Finally,a series of simulation experiments are conducted on high-dimensional datasets.The experi-mental results show that the hybrid feature selection algorithm can effectively solve the complex feature problems of multi-dimen-sional time series data.关键词
时间序列/云模型/多目标粒子群优化/混合特征选择/聚类算法Key words
time series/cloud model/multi-objective particle swarm optimization/hybrid feature selection/clustering algo-rithm分类
数理科学引用本文复制引用
刘小红,张人龙..数据驱动下基于时间序列云模型的特征选择聚类算法研究[J].统计与决策,2026,42(5):41-47,7.基金项目
国家自然科学基金资助项目(72561005 （）
72261005) （）
贵州省高校哲学社会科学实验室试点建设项目(GDJD202407) （GDJD202407）
贵州大学人文社会科学项目(GDYB2025009 （）
GDZD2025002) （）
