{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T17:55:46Z","timestamp":1785952546960,"version":"3.56.0"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["62076111"],"award-info":[{"award-number":["62076111"]}]},{"name":"Natural Science Foundation of China","award":["62006099"],"award-info":[{"award-number":["62006099"]}]},{"name":"Natural Science Foundation of China","award":["OBDMA202002"],"award-info":[{"award-number":["OBDMA202002"]}]},{"name":"Natural Science Foundation of China","award":["KYCX22_3826"],"award-info":[{"award-number":["KYCX22_3826"]}]},{"name":"Natural Science Foundation of China","award":["SJCX22_1905"],"award-info":[{"award-number":["SJCX22_1905"]}]},{"name":"Key Laboratory of Oceanographic Big Data Mining &amp; Application of Zhejiang Province","award":["62076111"],"award-info":[{"award-number":["62076111"]}]},{"name":"Key Laboratory of Oceanographic Big Data Mining &amp; Application of Zhejiang Province","award":["62006099"],"award-info":[{"award-number":["62006099"]}]},{"name":"Key Laboratory of Oceanographic Big Data Mining &amp; Application of Zhejiang Province","award":["OBDMA202002"],"award-info":[{"award-number":["OBDMA202002"]}]},{"name":"Key Laboratory of Oceanographic Big Data Mining &amp; Application of Zhejiang Province","award":["KYCX22_3826"],"award-info":[{"award-number":["KYCX22_3826"]}]},{"name":"Key Laboratory of Oceanographic Big Data Mining &amp; Application of Zhejiang Province","award":["SJCX22_1905"],"award-info":[{"award-number":["SJCX22_1905"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["62076111"],"award-info":[{"award-number":["62076111"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["62006099"],"award-info":[{"award-number":["62006099"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["OBDMA202002"],"award-info":[{"award-number":["OBDMA202002"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX22_3826"],"award-info":[{"award-number":["KYCX22_3826"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["SJCX22_1905"],"award-info":[{"award-number":["SJCX22_1905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures.<\/jats:p>","DOI":"10.3390\/sym14091821","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T09:51:09Z","timestamp":1662630669000},"page":"1821","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers"],"prefix":"10.3390","volume":"14","author":[{"given":"Qihang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1290-6112","authenticated-orcid":false,"given":"Pingxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2015.10.026","article-title":"Advances in three-way decisions and granular computing","volume":"91","author":"Fujita","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","unstructured":"Yao, Y.Y. 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