{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:36:11Z","timestamp":1760150171978,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T00:00:00Z","timestamp":1698019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Clustering problems are prevalent in areas such as transport and partitioning. Owing to the demand for centralized storage and limited resources, a complex variant of this problem has emerged, also referred to as the weakly balanced constrained clustering (WBCC) problem. Clusters must satisfy constraints regarding cluster weights and connectivity. However, existing methods fail to guarantee cluster connectivity in diverse scenarios, thereby resulting in additional transportation costs. In response to the aforementioned limitations, this study introduces a shelved\u2013retrieved method. This method embeds adjacent relationships during power diagram construction to ensure cluster connectivity. Using the shelved\u2013retrieved method, connected clusters are generated and iteratively adjusted to determine the optimal solutions. Further, experiments are conducted on three synthetic datasets, each with three objective functions, and the results are compared to those obtained using other techniques. Our method successfully generates clusters that satisfy the constraints imposed by the WBCC problem and consistently outperforms other techniques in terms of the evaluation measures.<\/jats:p>","DOI":"10.3390\/a16100492","type":"journal-article","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T01:41:21Z","timestamp":1698025281000},"page":"492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Shelved\u2013Retrieved Method for Weakly Balanced Constrained Clustering Problems"],"prefix":"10.3390","volume":"16","author":[{"given":"Xinxiang","family":"Hou","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Andong","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Lu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Zhouwang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, University of Science and Technology of China, Hefei 230026, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: A review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comput. 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