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Then, the objective function of the transfer rough clustering algorithm is optimized by using the differential evolution algorithm to enhance the robustness of the algorithm. It can overcome the sensitivity to initialized cluster centers and meanwhile achieve the global optimal clustering. The proposed algorithm is validated on different synthetic and real-world datasets. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with both traditional rough clustering algorithms and other state-of-the-art clustering algorithms.<\/jats:p>","DOI":"10.1007\/s40747-023-00987-8","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T08:03:35Z","timestamp":1677485015000},"page":"5033-5047","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Differential evolution-based transfer rough clustering algorithm"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-9573","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Chaofei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8774-8625","authenticated-orcid":false,"given":"Hanqiang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"987_CR1","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","volume":"27","author":"JZ Huang","year":"2005","unstructured":"Huang JZ, Ng MK, Rong H, Li Z (2005) Automated variable weighting in k-means type clustering. 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