Abstract:Clustering is an important task with applications in many fields of computer science. We study the fully dynamic setting in which we want to maintain good clusters efficiently when input points (from a metric space) can be inserted and deleted. Many clustering problems are APX-hard but admit polynomial time O(1)-approximation algorithms. Thus, it is a natural question whether we can maintain O(1)approximate solutions for them in subpolynomial update time, against adaptive and oblivious adversaries. Only a few … Show more
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