---
res:
  bibo_abstract:
  - Clustering is one of the most fundamental problems in unsupervised learning with
    a large number of applications. However, classical clustering algorithms assume
    that the data is static, thus failing to capture many real-world applications
    where data is constantly changing and evolving. Driven by this, we study the metric
    k-center clustering problem in the fully dynamic setting, where the goal is to
    efficiently maintain a clustering while supporting an intermixed sequence of insertions
    and deletions of points. This model also supports queries of the form (1) report
    whether a given point is a center or (2) determine the cluster a point is assigned
    to. We present a deterministic dynamic algorithm for the k-center clustering problem
    that provably achieves a (2 + ∊)-approximation in nearly logarithmic update and
    query time, if the underlying metric has bounded doubling dimension, its aspect
    ratio is bounded by a polynomial and ∊ is a constant. An important feature of
    our algorithm is that the update and query times are independent of k. We confirm
    the practical relevance of this feature via an extensive experimental study which
    shows that for large values of k, our algorithmic construction outperforms the
    state-of-the-art algorithm in terms of solution quality and running time.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Gramoz
      foaf_name: Goranci, Gramoz
      foaf_surname: Goranci
  - foaf_Person:
      foaf_givenName: Monika H
      foaf_name: Henzinger, Monika H
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=540c9bbd-f2de-11ec-812d-d04a5be85630
    orcid: 0000-0002-5008-6530
  - foaf_Person:
      foaf_givenName: Dariusz
      foaf_name: Leniowski, Dariusz
      foaf_surname: Leniowski
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Schulz, Christian
      foaf_surname: Schulz
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Svozil, Alexander
      foaf_surname: Svozil
  bibo_doi: 10.1137/1.9781611976472.11
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2164-0300
  dct_language: eng
  dct_publisher: Society for Industrial and Applied Mathematics@
  dct_title: Fully dynamic k-center clustering in low dimensional metrics@
...