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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Vydané v:	Expert systems with applications Ročník 150; s. 113288
Hlavní autori:	Kim, Hyunjoong, Kim, Han Kyul, Cho, Sungzoon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 15.07.2020 Elsevier BV
Predmet:	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
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