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      Research and Development of User Clustering-Based Content Similarity Algorithms in Dance-Assisted Choreography Techniques

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      1 , , 2
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          With the gradual development of digital information and software computing capabilities, the use of computers in dance-assisted choreography is becoming more and more widespread. But although the level of computers is now in rapid development, the technical level of using computers in dance choreography is not yet very mature, technical support is not in place, dance-assisted choreography is not effective, and the existing technical level is not yet able to meet the new needs of dance choreography. In order to improve the dance-assisted choreography technology and provide a more complete educational user interface for dance-assisted choreography, the content similarity algorithm of user clustering has a wide range of operations and a strong ability to calculate the amount of data, combined with the computer to apply the content similarity algorithm of user clustering in dance-assisted choreography technology to build a dance-assisted choreography system based on user clustering. The article proposes three major methods based on collaborative filtering algorithm of user clustering, collaborative filtering algorithm based on similarity class and user preference, and fuzzy cluster analysis of users and analyses their principles. In the experimental part, the performance of IBCF algorithm and collaborative filtering algorithm in dance-assisted choreography system is compared and analysed to observe the change of MAE value under the change of user similarity with number under different k values of cluster classes. The experimental results found that the MAE values of the IBCF algorithm and the collaborative filtering algorithm in the system were at 0.84 and 0.76, respectively, with a difference of about 8% between the two MAE values. The smaller the MAE value, the higher the effectiveness in the dance-assisted choreography technique. Applying the clustering algorithm to the system to make local adjustments and analysis of dance movement paths, it can grasp the choreography rules more precisely and innovate the choreography techniques.

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                23 September 2022
                : 2022
                : 1364835
                Affiliations
                1School of Architecture and Art, Central South University, Changsha 410000, Hunan, China
                2College of Music and Dance, Huaihua University, Huaihua 418000, Hunan, China
                Author notes

                Academic Editor: Akshi Kumar

                Author information
                https://orcid.org/0000-0003-0808-1792
                Article
                10.1155/2022/1364835
                9525197
                36188680
                48d39f07-ca03-40ab-bc49-223bd3a59be8
                Copyright © 2022 Yanyan Wu and Min Liu.

                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.

                History
                : 20 July 2022
                : 26 August 2022
                : 2 September 2022
                Categories
                Research Article

                Neurosciences
                Neurosciences

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