{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:43:33Z","timestamp":1773819813009,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"43","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Clustering is a long-standing research problem and a fundamental tool in AI and data analysis. The traditional k-center problem, known as a fundamental theoretical challenge in clustering, has a best possible approximation ratio of 2, and any improvement to a ratio of 2 - \u03b5 would imply P = NP. In this work, we study the constrained k-center clustering problem, where instance-level cannot-link (CL) and must-link (ML) constraints are incorporated as background knowledge. Although general CL constraints significantly increase the hardness of approximation, previous work has shown that disjoint CL sets permit constant-factor approximations. However, whether local search can achieve such a guarantee in this setting remains an open question. To this end, we propose a novel local search framework based on a transformation to a dominating matching set problem, achieving the best possible approximation ratio of 2. The experimental results on both real-world and synthetic datasets demonstrate that our algorithm outperforms baselines in solution quality.<\/jats:p>","DOI":"10.1609\/aaai.v40i43.41026","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:39:27Z","timestamp":1773815967000},"page":"36982-36990","source":"Crossref","is-referenced-by-count":0,"title":["Approximation Algorithm for Constrained k-Center Clustering: A Local Search Approach"],"prefix":"10.1609","volume":"40","author":[{"given":"Chaoqi","family":"Jia","sequence":"first","affiliation":[]},{"given":"Longkun","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Kewen","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Xue","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/41026\/44987","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/41026\/44987","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:39:27Z","timestamp":1773815967000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/41026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"43","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i43.41026","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}