之前的很多聚类工作都集中在原始数据上而忽略了高维数据存在的噪声和不相关

Many previous clustering work focused on the original data and ignored the noise and irrelevant information in the high-dimensional data. It can be found that when directly using high-dimensional data as the input of the clustering task, it may cause a serious decrease in performance and an increase in calculation complexity.

Much of the previous clustering effort focused on raw data while ignoring the noise and irrelevant information of high-dimensional data. It can be found that when high-dimensional data is used directly as input for clustering tasks, performance can be severely degraded and computational complexity increased.

Many previous clustering work focused on the original data and ignored the noise and irrelevant information of high-dimensional data. It can be found that directly using high-dimensional data as the input of clustering task may lead to serious performance degradation and increased computational complexity.<br>