A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining

Juan Li; Geng Sun; Juan Li; Geng Sun

doi:10.3934/era.2023303

Electronic Research Archive

2023, Volume 31, Issue 10: 5959-5975. doi: 10.3934/era.2023303

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A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining

Juan Li ¹,
Geng Sun ^{2,3
,
,}

1.
School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China
2.
School of Computer Engineering, Chongqing College of Humanities, Science and Technology, Chongqing 401524, China
3.
Vermilion Cloud, Sydney 2000, Australia

Received: 11 June 2023 Revised: 05 August 2023 Accepted: 14 August 2023 Published: 05 September 2023

In order to improve the application of teaching resources and reduce delays in the integration process of multimedia network, a rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining is proposed. Bayesian partition is used to preprocess the multimedia network teaching resources (MNTR), adjusting the recognition probability of MNTR in each partition based on its attributes. By performing Bayesian quantitative classification using samples of MNTR, the prior probability is adjusted through maximization analysis. The partitioned resources undergo sample data mining to obtain the data category collection of all MNTR. A prediction model is then built to forecast the demand for teaching resources at specific times in the future. MNTR can be rationally allocated based on the prediction results. Experimental results demonstrate that this method reduces delays in MNTR application and improves the accuracy and utilization of teaching resources.
- Bayesian,
- partition data mining,
- multimedia network,
- teaching reform resources,
- rational allocation,
- resource forecast
Citation: Juan Li, Geng Sun. A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining[J]. Electronic Research Archive, 2023, 31(10): 5959-5975. doi: 10.3934/era.2023303

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Abstract

In order to improve the application of teaching resources and reduce delays in the integration process of multimedia network, a rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining is proposed. Bayesian partition is used to preprocess the multimedia network teaching resources (MNTR), adjusting the recognition probability of MNTR in each partition based on its attributes. By performing Bayesian quantitative classification using samples of MNTR, the prior probability is adjusted through maximization analysis. The partitioned resources undergo sample data mining to obtain the data category collection of all MNTR. A prediction model is then built to forecast the demand for teaching resources at specific times in the future. MNTR can be rationally allocated based on the prediction results. Experimental results demonstrate that this method reduces delays in MNTR application and improves the accuracy and utilization of teaching resources.

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