{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:45:28Z","timestamp":1773819928029,"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>Many real-world applications call for incorporating fairness constraints into the k-center clustering problem, where the dataset is partitioned into m demographic groups, each with a specified upper bound on the number of centers to ensure fairness. Focusing on big data scenarios, this paper addresses the problem in a streaming setting, where data points arrive\nsequentially in a continuous stream. Leveraging a structure called the \u03bb-independent center set, we propose a one-pass streaming algorithm that first computes a reserved set of points\nduring the streaming process. In the post-streaming process, we then select centers from the reserved point set by analyzing three possible cases and transforming the most complex\none into a specially constrained vertex-cover problem on an auxiliary graph. Our algorithm achieves an approximation ratio of 5 + ? and memory complexity O(k log ?), where ? is the aspect ratio and ? &gt; 0 is any small constant. Furthermore, we extend our approach to semi-structured data streams, where data points arrive in groups. In this setting, we present\na (3 + ?)-approximation algorithm for m = 2, which can be readily adapted to solve the offline fair k-center problem, achieving an approximation ratio of 3 that matches the current\nstate of the art. Lastly, we conduct extensive experiments to evaluate the performance of our approaches, demonstrating that they outperform existing baselines in both clustering cost\nand runtime efficiency.<\/jats:p>","DOI":"10.1609\/aaai.v40i43.41022","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:42:06Z","timestamp":1773816126000},"page":"36946-36954","source":"Crossref","is-referenced-by-count":0,"title":["Improved Streaming Algorithm for Fair k-Center Clustering"],"prefix":"10.1609","volume":"40","author":[{"given":"Longkun","family":"Guo","sequence":"first","affiliation":[]},{"given":"Zeyu","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Chaoqi","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Chen","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\/41022\/44983","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/41022\/44983","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:42:07Z","timestamp":1773816127000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/41022"}},"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.v40i43.41022","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]]}}}