School of Architecture and Art, Central South University, Changsha 410000, Hunan, China.
College of Music and Dance, Huaihua University, Huaihua 418000, Hunan, China.
Comput Intell Neurosci. 2022 Sep 23;2022:1364835. doi: 10.1155/2022/1364835. eCollection 2022.
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 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.
随着数字信息和软件计算能力的逐步发展，计算机在舞蹈辅助编舞中的应用越来越广泛。但尽管计算机的水平现在正在快速发展，但是在舞蹈编舞中使用计算机的技术水平还不是很成熟，技术支持不到位，舞蹈辅助编舞效果不佳，现有的技术水平还不能满足舞蹈编舞的新需求。为了提高舞蹈辅助编舞技术，为舞蹈辅助编舞提供更完整的教育用户界面，用户聚类的内容相似性算法具有广泛的操作和强大的数据计算能力，结合计算机将用户聚类的内容相似性算法应用于舞蹈辅助编舞技术中，构建基于用户聚类的舞蹈辅助编舞系统。本文提出了基于用户聚类的协同过滤算法、基于相似类和用户偏好的协同过滤算法以及用户的模糊聚类分析三大方法，并分析了它们的原理。在实验部分，对 IBCF 算法和协同过滤算法在舞蹈辅助编舞系统中的性能进行了比较和分析，观察了在不同聚类类别值下，用户相似性随用户数量变化时 MAE 值的变化。实验结果发现，系统中 IBCF 算法和协同过滤算法的 MAE 值分别为 0.84 和 0.76，两者的 MAE 值相差约 8%。MAE 值越小，在舞蹈辅助编舞技术中的效果越高。将聚类算法应用于系统，对舞蹈动作路径进行局部调整和分析，可以更准确地把握编舞规则，创新编舞技术。
