Abstract
In order to improve the stability of heterogeneous big data mining operations in complex attribute environment, such as data analysis and cleaning, a heterogeneous big data intelligent clustering algorithm is established. The data cleaning classification method is applied to clean the parameter space in complex attribute environment, and the regular term of sparse subspace clustering is introduced to eliminate the irrelevant and redundant information of heterogeneous big data, and the intelligent clustering index of heterogeneous big data is obtained. By measuring the clustering results, the design of heterogeneous big data intelligent clustering algorithm in complex attribute environment is completed. The experimental results show that the heterogeneous big data intelligent clustering algorithm in complex attribute environment has strong stability in the process of data analysis and cleaning.
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Y., Zhai, Jl. (2021). Heterogeneous Big Data Intelligent Clustering Algorithm in Complex Attribute Environment. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-030-67871-5_32
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DOI: https://doi.org/10.1007/978-3-030-67871-5_32
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