K-NNDP: K-means algorithm based on nearest neighbor density peak optimization and outlier removal

Liao, Jiyong; Wu, Xingjiao; Wu, Yaxin; Shu, Juelin

doi:10.1016/j.knosys.2024.111742

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2024.111742

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K-NNDP: K-means algorithm based on nearest neighbor density peak optimization and outlier removal

Jiyong Liao,

Xingjiao Wu,

Yaxin Wu

et al.

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2024

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Enrichment and Expansion of Classical Dance Teaching Content under the Perspective of Innovative Education

Liu

2024

Applied Mathematics and Nonlinear Sciences

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This paper proposes a collaborative filtering-based learning resource recommendation algorithm to help learners find interesting resources in a huge amount of teaching resources and improve learning efficiency and learning interest so that the classical dance teaching content can be further enriched and expanded. By improving the K-means clustering algorithm, the article realizes the clustering of students’ classical dance teaching content preferences and further proposes a collaborative filtering algorithm based on ratings and attribute preferences of classical dance teaching resources, which takes into account the relationship between users’ needs and the attributes of the resources in order to complete the personalized recommendation of classical dance teaching resources for students. The article finally takes a dance teaching classroom as an example for empirical research. In the two groups of classical dance performance scoring a single total average score, the experimental group’s performance of dance movements is better than that of the control group, the average score of the experimental group’s pre-test is higher than the average score of the control group’s pre-test by 8±1.8 points, and the average score of the experimental group’s post-test is higher than the average score of the control group’s post-test by 9±2 points.

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Enrichment and Expansion of Classical Dance Teaching Content under the Perspective of Innovative Education

Liu

2024

Applied Mathematics and Nonlinear Sciences

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Research on e-commerce user segmentation and customized marketing strategy based on cluster analysis

Zhao,

Niu,

Lin

et al. 2024

Applied Mathematics and Nonlinear Sciences

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E-commerce user segmentation is the basis for enterprises to accurately formulate marketing strategies and successfully manage their customer base. With the rapid development of e-commerce, this paper improves the traditional K-means clustering algorithm and proposes a SAPK-means algorithm, which effectively excludes the noisy data and isolated points in the dataset and obtains the high-quality initial clustering center. Company A’s e-commerce platform is used to apply the SAPK-means algorithm for customer segmentation, and the results are analyzed in detail before proposing targeted marketing strategies. The customized marketing strategy’s effect is evaluated through sales and platform user satisfaction. Through experimental testing, this paper concludes that the five types of segmented customer groups account for 9.46%, 18.43%, 36.95%, 21.91%, and 13.24% of the total number of samples, respectively, known as the “platinum customer group”, “gold customer group “Platinum customer group”, “Gold customer group”, “Silver customer group”, “Copper customer group” and “Iron customer group”, respectively.

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K-NNDP: K-means algorithm based on nearest neighbor density peak optimization and outlier removal

Cited by 2 publications

References 32 publications

Enrichment and Expansion of Classical Dance Teaching Content under the Perspective of Innovative Education

Enrichment and Expansion of Classical Dance Teaching Content under the Perspective of Innovative Education

Research on e-commerce user segmentation and customized marketing strategy based on cluster analysis

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