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High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak
Joint Authors
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-28
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Information Technology and Computer Science
Abstract EN
This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers.
We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm.
The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means.
Our proposed algorithm is validated using seven text datasets.
Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.
American Psychological Association (APA)
Sun, Yujia& Platoš, Jan. 2020. High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1214852
Modern Language Association (MLA)
Sun, Yujia& Platoš, Jan. High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1214852
American Medical Association (AMA)
Sun, Yujia& Platoš, Jan. High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1214852
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214852