Correction to: Feature selection and risk prediction for patients with coronary artery disease using data mining

Idris, Nashreen Md; Chiam, Yin Kia; Varathan, Kasturi Dewi; Ahmad, Wan Azman Wan; Chee, Kok Han; Liew, Yih Miin

doi:10.1007/s11517-022-02506-2

Cited by 2 publications

(1 citation statement)

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“…Adjust the data to reduce the number of partition data used. If the similarity of behavior feature similarity results after model training is less than 1, they are classified as the same type of data [14].…”

Section: Preparation For the Experimentsmentioning

confidence: 99%

Mining potential customers of electric power industry based on customer behavior analysis

Huang¹,

Feng

Wang³

et al. 2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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With the continuous development of China's electricity market, the characteristics of diversification of electricity market demand are gradually highlighted. How to optimize the marketing strategy to meet the electricity demand of different customers and improve the core competitiveness has become an urgent requirement of electric power enterprises. Aiming at the problem of long mining time when using traditional mining methods to mine suitable potential customers in the power industry, a mining method of potential customers in the power industry based on customer behavior analysis is designed. Firstly, some data of potential customers in power industry are extracted and preprocessed. Then, the customer behavior analysis method is used to extract the characteristics of customer behavior. Finally, the mining model of potential customers in the power industry is constructed to realize the mining of potential customers. The experimental results show that the potential customer mining method based on customer behavior analysis is faster than the traditional method, which provides a feasible research direction for the expansion of the potential customer market in the power industry and has practical research value.

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Section: Preparation For the Experimentsmentioning

confidence: 99%