2021
DOI: 10.1155/2021/5349916
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Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Abstract: In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone … Show more

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Cited by 4 publications
(3 citation statements)
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“…is paper studies opera art in combination with in-depth learning and evolution strategy. It proposes the process of in-depth learning and evolution to jointly explore the inspiration of opera art creation [18]. e following is the process of creation of drama art based on indepth learning and evolution strategy:…”
Section: Research Process Based On Depth Information and Evolution St...mentioning
confidence: 99%
“…is paper studies opera art in combination with in-depth learning and evolution strategy. It proposes the process of in-depth learning and evolution to jointly explore the inspiration of opera art creation [18]. e following is the process of creation of drama art based on indepth learning and evolution strategy:…”
Section: Research Process Based On Depth Information and Evolution St...mentioning
confidence: 99%
“…The method being offered creates a database for the clustering of big data by first designing and building the set of attributes of the major data function sequential training set and then simultaneously establishing a second-order cone optimization method to rectify the big data. In doing so, the method is able to provide a database for the cluster analysis of big data [16].…”
mentioning
confidence: 99%
“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”
mentioning
confidence: 99%