Abstract

In order to solve the problems of reading delay in data mining of the Internet of Things database, a clustering mining algorithm of the Internet of Things database based on Python language is proposed. We designed an improved crawler algorithm based on the open-source structure of scratch through Python language, judge the similarity of recruitment data topics in the Internet of Things database through Bayesian classifier, and crawl the recruitment data in the Internet of Things database: the number of keywords in the text space, the degree of keyword extraction, and the number of keyword data in the text space. The time series model is used to eliminate the delay of text features. On this basis, the semi-supervised learning and semi-cluster analysis method is used to construct the corresponding classifier, complete the adaptive classification process of the text data stream and realize the clustering mining of the Internet of Things database based on Python language. The experimental results show that this method has a low reading delay, and can mine the attention, number of posts and click time frequency of the Internet of Things database from which the recruitment data are obtained.

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Cite this article

@article{cai-6205,
  author  = {Fang Wan and Ying Liu},
  title   = {Clustering Mining Algorithm of Internet of Things Database Based on Python Language},
  journal = {Computing and Informatics},
  volume  = {42},
  number  = {5},
  pages   = {1136--1157},
  year    = {2023},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2023_5_1136}
}
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