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The dimensional reduction technique is based on the Johnson Lindenstrauss lemma algorithm, preserving the relative distance between data samples. This can make clustering easier without affecting the clustering results. Moreover, by reducing dimensionality and sharing information among sub-data in collaborative clustering, it is possible to improve the performance and accuracy of hyperspectral remote sensing image analysis results. The experiments conducted on two hyperspectral image data sets with five validity indexes show that the proposed methods perform better compared with the other methods.<\/jats:p>","DOI":"10.3233\/jifs-230511","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T10:08:20Z","timestamp":1692698900000},"page":"7739-7752","source":"Crossref","is-referenced-by-count":1,"title":["Features reduction collaborative fuzzy clustering for hyperspectral remote sensing images analysis"],"prefix":"10.1177","volume":"45","author":[{"given":"Trong Hop","family":"Dang","sequence":"first","affiliation":[{"name":"Hanoi University of Industry, Hanoi, Vietnam"}]},{"given":"Viet Duc","family":"Do","sequence":"additional","affiliation":[{"name":"Le Quy Don Technical University, Hanoi, Vietnam"}]},{"given":"Dinh Sinh","family":"Mai","sequence":"additional","affiliation":[{"name":"Le Quy Don Technical University, Hanoi, Vietnam"}]},{"given":"Long 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