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Zhang Z, Talha M. Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability. SECURITY AND COMMUNICATION NETWORKS 2022; 2022:1-9. [DOI: 10.1155/2022/4852736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
In view of the present poor impact of multi relational data clustering, a multirelational data clustering effectiveness analysis model based on trusted probability is created. The construction of the multirelational data clustering trusted probability evaluation model, standardization of the multirelational data clustering trusted probability evaluation index, optimization of the multirelational data clustering trusted probability analysis process, and data clustering processing quality and finally, investigations show that the validity analysis model of multirelational data clustering based on credible probability is more effective in practice and satisfies the research goals entirely.
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Affiliation(s)
- Zhongzhen Zhang
- Hunan Biological and Electromechanical Polytechnic, Changsha 410127, China
| | - Muhammad Talha
- Department of Computer Science, Superior University, Lahore, Pakistan
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Hu S, Wang R, Ye Y. Interactive information bottleneck for high-dimensional co-occurrence data clustering. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Li Y, Zhao Q, Luo K. Multi-objective soft subspace clustering in the composite kernel space. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.02.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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