High Density Subspace Clustering Algorithm for High Dimensional Data

Highdimensional data have the characteristics of sparsity and vulnerability to dimension disaster, which makes it is difficult to ensure the precision and efficiency of high dimensional data clustering Therefore the method of subspace clustering is adopted to reduce the impact of sparsity and dimens...

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Bibliographic Details
Main Authors: WAN Jing, ZHENG Longjun, HE Yunbin, LI Song
Format: Article
Language:zho
Published: Harbin University of Science and Technology Publications 2020-08-01
Series:Journal of Harbin University of Science and Technology
Subjects:
Online Access:https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1909
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