A Review of Time Series Data Mining Methods Based on Cluster Analysis

被引：2

作者：

Lu, Yifan ^{[1
]}

Zhang, Yufeng ^{[2
]}

Chen, Shuangshuang ^{[2
]}

机构：

[1] Hohai Univ, Business Sch, Changzhou, Jiangsu, Peoples R China

[2] Hohai Univ, Coll Mech & Elect Engn, Changzhou, Jiangsu, Peoples R China

来源：

2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年

关键词：

data mining; time series; clustering analysis; big data;

D O I：

10.1109/CCDC58219.2023.10326549

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, time series are used as the analyzed values and the focus is on their data mining methods. This detailed evaluation highlights the clustering algorithms in time series data mining. The traditional k-means algorithm and its improvement and three new clustering analysis algorithms (hierarchical cluster analysis, clustering algorithm based on time series data features, and density-based clustering algorithm) are mainly introduced. In addition we analyze the future research directions and further optimization of time series analysis. In summary, this paper focuses on clustering algorithms in time series data mining.

引用

页码：4198 / 4202

页数：5

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