Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling

作者：

Highlights：

• Spherical k-means for document clustering is improved to overcome its weaknesses.

• Our method ensures dispersed initial points with faster computation time.

• Our method preserves sparsity of centroid vectors for better interpretability.

• We provide unsupervised document cluster labeling method.

摘要

•Spherical k-means for document clustering is improved to overcome its weaknesses.•Our method ensures dispersed initial points with faster computation time.•Our method preserves sparsity of centroid vectors for better interpretability.•We provide unsupervised document cluster labeling method.

论文关键词：Spherical k-means,Document clustering,k-means initialization,Sparse vector projection,Clustering labeling

论文评审过程：Received 30 September 2018, Revised 23 July 2019, Accepted 5 February 2020, Available online 6 February 2020, Version of Record 19 February 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113288