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Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining

With the development of information technology, the influence of traditional media is weakening day by day. In view of this, based on the Internet of things data mining technology, this study improves the k-means algorithm, and designs a new media precision marketing system, which combines new media...

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Detalles Bibliográficos
Autor principal: Liu, Yinuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106486/
https://www.ncbi.nlm.nih.gov/pubmed/35571712
http://dx.doi.org/10.1155/2022/8193800
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author Liu, Yinuo
author_facet Liu, Yinuo
author_sort Liu, Yinuo
collection PubMed
description With the development of information technology, the influence of traditional media is weakening day by day. In view of this, based on the Internet of things data mining technology, this study improves the k-means algorithm, and designs a new media precision marketing system, which combines new media with traditional media and provides a new marketing model for traditional media. The results show that the accuracy of the improved k-means algorithm finally reaches about 93%, which is much higher than that of similar algorithms. It can be seen that the improved k-means algorithm has better performance. In the application experiment, this study can effectively find the new media activities with the highest user preference, and the impact of the two websites' application of precision marketing system on users has also increased. It can be seen that the precision marketing system designed this time is more effective.
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spelling pubmed-91064862022-05-14 Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining Liu, Yinuo Comput Intell Neurosci Research Article With the development of information technology, the influence of traditional media is weakening day by day. In view of this, based on the Internet of things data mining technology, this study improves the k-means algorithm, and designs a new media precision marketing system, which combines new media with traditional media and provides a new marketing model for traditional media. The results show that the accuracy of the improved k-means algorithm finally reaches about 93%, which is much higher than that of similar algorithms. It can be seen that the improved k-means algorithm has better performance. In the application experiment, this study can effectively find the new media activities with the highest user preference, and the impact of the two websites' application of precision marketing system on users has also increased. It can be seen that the precision marketing system designed this time is more effective. Hindawi 2022-05-06 /pmc/articles/PMC9106486/ /pubmed/35571712 http://dx.doi.org/10.1155/2022/8193800 Text en Copyright © 2022 Yinuo Liu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Yinuo
Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining
title Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining
title_full Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining
title_fullStr Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining
title_full_unstemmed Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining
title_short Integration Path Analysis of Traditional Media and New Media Based on Internet of Things Data Mining
title_sort integration path analysis of traditional media and new media based on internet of things data mining
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106486/
https://www.ncbi.nlm.nih.gov/pubmed/35571712
http://dx.doi.org/10.1155/2022/8193800
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