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通过使用无监督机器学习模型识别网站访问背后的意图

Judah Soobramoney1, Retius Chifurira1, Temesgen Zewotir1

  • 1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.

Annals of data science
|August 26, 2025
PubMed
概括

这项研究使用无监督的机器学习来分析网站访问者数据,确定了五个关键用户意图. 对于理解在线访问动机而言, 层次聚类是最有效的.

关键词:
立方集群的标准在DBScan中牙周图谷歌分析跟踪层次化的集群K-意味着在线网站访问轮的系数无监督机器学习

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Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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