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

•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. Due to...

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Bibliographic Details

Published in:	Expert systems with applications Vol. 150; p. 113288
Main Authors:	Kim, Hyunjoong, Kim, Han Kyul, Cho, Sungzoon
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 15.07.2020 Elsevier BV
Subjects:	Centroids Clustering Clustering labeling Datasets Dispersion Document clustering k-means initialization Labeling Sparse vector projection Spherical k-means Spherical k-means Document clustering Sparse vector projection Clustering labeling k-means initialization
ISSN:	0957-4174, 1873-6793
Online Access:	Get full text
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