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- W4283450178 abstract "The traditional filling method for network big data matrix has poor filling effect and suffers from noise. Therefore, a filling algorithm for network big data high rank matrix based on density peak clustering is proposed. The missing data are replaced by small-interval data, the information entropy of the high rank matrix of network big data is calculated, the density peak clustering algorithm is optimized through the cluster center selection strategy, the block data set is obtained through the unknown block method, and the block filling is realized by the host filling algorithm. Experimental results show that the filling accuracy of the proposed algorithm is as high as 0.895, and the loss rate is between 2% and 12%." @default.
- W4283450178 created "2022-06-26" @default.
- W4283450178 creator A5013447476 @default.
- W4283450178 date "2022-06-25" @default.
- W4283450178 modified "2023-10-14" @default.
- W4283450178 title "Algorithm for Filling High Rank Matrix of Network Big Data Based on Density Peak Clustering" @default.
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- W4283450178 doi "https://doi.org/10.1155/2022/8662238" @default.
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