基于改进SSA-<italic style="font-style: italic">K</italic>means算法的网站用户分群研究

雷雨霄

doi:10.11959/j.issn.1000-0801.2026023

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基于改进SSA-Kmeans算法的网站用户分群研究

更新时间：2026-01-21

- 基于改进SSA-Kmeans算法的网站用户分群研究
- Research on website user clustering based on improved SSA-Kmeans algorithm
- 电信科学 2026年页码：1-9
- 作者机构：
  
  中移物联网有限公司，重庆 401121
- 作者简介：
  
  [ "雷雨霄（1999- ），男，硕士，中移物联网有限公司数智化部产品开发工程师，主要研究方向为用户访问量预测与用户行为特征分析。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2026023
  中图分类号： TP391;TN-9
- 修回：2025-07-04，
  
  录用：2025-07-07，
  
  网络出版：2026-01-06，
- 稿件说明：
移动端阅览
雷雨霄.基于改进SSA-Kmeans算法的网站用户分群研究[J].电信科学,

LEI Yuxiao.Research on website user clustering based on improved SSA-Kmeans algorithm[J].Telecommunications Science,
雷雨霄.基于改进SSA-Kmeans算法的网站用户分群研究[J].电信科学, DOI：10.11959/j.issn.1000−0801.2026023.

LEI Yuxiao.Research on website user clustering based on improved SSA-Kmeans algorithm[J].Telecommunications Science, DOI：10.11959/j.issn.1000−0801.2026023.

摘要

在数字化时代，精准把握用户需求是网站实现精准营销与个性化服务的关键。针对该场景，提出了一种改进的奇异谱分析

均值（singular spectrum analysis

-means，SSA-

means）算法，有效地解决了传统SSA中窗口长度及特征分量须手动选取的低效问题。该算法先利用SSA提取用户访问数据的核心特征分量，再结合

-means进行聚类分析。实验结果表明，该算法显著地提升了聚类效果，戴维斯堡丁指数较直接聚类和小波变化去噪后聚类分别降低了0.407 1和0.067 2，簇划分更精准。基于优化后的聚类结果，进一步制定了差异化运营策略，针对不同用户群体提供定制化服务。这一方法为网站精准营销和用户留存提供了高效的解决方案，具有重要的实践应用价值。

Abstract

In the digital age

accurately grasping user needs is the key to acheive accurate marketing and personalized services. Aiming at this scenario

an improved singular spectrum analysis

-means (SSA-

means) algorithm was proposed

which effectively solved the inefficient problem of manual selection of window length and feature components in the traditional SSA. Firstly

the SSA was used to extract the core feature components of user access data in this algorithm. Then

-means was combined for cluster analysis. The experimental results show that the clustering effect is significantly improved. The Davis-Burdin index is 0.407 1 and 0.067 2 lower than that of direct clustering and wavelet transform denoising clustering

respectively. Moreover

the cluster division is more accurate. Based on the optimized clustering results

a differentiated operation strategy was further developed to provide customized services for different user groups. This method provides an efficient solution for website precision marketing and user retention

and has important practical application value.

关键词

Keywords

references

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基于改进SSA-Kmeans算法的网站用户分群研究

Research on website user clustering based on improved SSA-Kmeans algorithm

DOI：10.11959/j.issn.1000-0801.2026023

摘要

Abstract

关键词

Keywords

references