Research on Improvement of Clustering Algorithm for Periodic Time-series Data

GUO Xiu-zhen; LU Jian-feng; TANG Jiu-bin

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on Improvement of Clustering Algorithm for Periodic Time-series Data

更新时间：2026-09-04

- Research on Improvement of Clustering Algorithm for Periodic Time-series Data
- Vol. 28, Issue 10, Pages: 118-121(2011)
- 作者机构：
  
  1. 1 南京理工大学计算机科学与技术学院,江苏,南京,210094
  2. 2 中国电信江苏公司,江苏,南京,210037
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
郭秀珍, 陆建峰, 汤九斌. 周期性时间序列数据聚类算法的改进研究[J]. 微电子学与计算机, 2011, 28(10): 118-121.

GUO Xiu-zhen, LU Jian-feng, TANG Jiu-bin. Research on Improvement of Clustering Algorithm for Periodic Time-series Data[J]. 2011, 28(10): 118-121.
郭秀珍, 陆建峰, 汤九斌. 周期性时间序列数据聚类算法的改进研究[J]. 微电子学与计算机, 2011, 28(10): 118-121. DOI：

GUO Xiu-zhen, LU Jian-feng, TANG Jiu-bin. Research on Improvement of Clustering Algorithm for Periodic Time-series Data[J]. 2011, 28(10): 118-121. DOI：

摘要

聚类分析是时间数据序列分析的一种常用手段

现有的聚类算法通常从相似性度量方面进行改进.实际的时间序列数据往往具有一定的周期性和连续性

现有的算法往往忽略时间序列数据周期性和连续性特点对聚类算法的影响.对此问题进行了研究

尝试采用延拓的方法来解决该问题

从而改善聚类的效果.初步的实验结果表明了该方法的可行性和有效性.

Abstract

Clustering analysis is a common means of time-series data analysis.The existing clustering algorithm for the time-series data is improved from the similarity measure aspects

but the actual time-series data tend to have a certain periodicity and continuity.The existing algorithms often ignore the impact of periodicity and continuity on time-series data clustering algorithms.This issue is researched in this paper and extension method is used to solve this problem.Preliminary results show the feasibility and effectiveness.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Steganography model guided by attention mechanism and improved dense convolution

Research on the design of blockchain-based AI-generated content traceability framework

Design of ultra-low power real-time clock circuits

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰