High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak. (28th October 2020)

• Record Type: Journal Article
• Authors: Sun, Yujia; Platoš, Jan
• Other Names: Lee Chao-Yang Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2020(2020)
• Publication Date: 2020-10-28
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2020/8881112
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : 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.