In order to speed up [K]-means computation and find the optimal clustered subspace, the data are projected using a specific transformation matrix, and the feature space is divided into clustered space and noise space. The former contains all spatial structure information, while the latter does not contain any information. The noise space is discarded and [K]-means is performed in the clustering space. The algorithm is different from PCA [K]-means in that it first reduces dimension and then clusters, but achieves the effect of dimension selection in the iteration process, and feeds the retained dimension back to the next iteration. At the same time, the dimension information of clustered space is automatically found without introducing additional parameters. Experiments show that the accuracy and computation time of the AC [K]-means algorithm are greatly improved compared with the existing similar algorithms.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1811-0380