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@INBOOK{Adinets:138003,
      author       = {Adinets, Andrey and Kraus, Jiri and Meinke, Jan and
                      Pleiter, Dirk},
      title        = {{GPUMAFIA}: {E}fficient {S}ubspace {C}lustering with
                      {MAFIA} on {GPU}s},
      volume       = {8097},
      address      = {New York},
      publisher    = {Springer New York},
      reportid     = {FZJ-2013-04288},
      series       = {Lecture Notes in Computer Science},
      pages        = {838-849},
      year         = {2013},
      note         = {$10.1007/978-3-642-40047-6_83$},
      comment      = {Euro-Par 2013 Parallel Processing},
      booktitle     = {Euro-Par 2013 Parallel Processing},
      abstract     = {Clustering, i.e., the identification of regions of similar
                      objects in a multi-dimensional data set, is a standard
                      method of data analytics with a large variety of
                      applications. For high-dimensional data, subspace clustering
                      can be used to find clusters among a certain subset of data
                      point dimensions and alleviate the curse of
                      dimensionality.In this paper we focus on the MAFIA subspace
                      clustering algorithm and on using GPUs to accelerate the
                      algorithm. We first present a number of algorithmic changes
                      and estimate their effect on computational complexity of the
                      algorithm. These changes improve the computational
                      complexity of the algorithm and accelerate the sequential
                      version by 1–2 orders of magnitude on practical datasets
                      while providing exactly the same output. We then present the
                      GPU version of the algorithm, which for typical datasets
                      provides a further 1–2 orders of magnitude speedup over a
                      single CPU core or about an order of magnitude over a
                      typical multi-core CPU. We believe that our faster
                      implementation widens the applicability of MAFIA and
                      subspace clustering.},
      month         = {Aug},
      date          = {2013-08-26},
      organization  = {Euro-Par 2013, Aachen (Germany), 26
                       Aug 2013 - 30 Aug 2013},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {411 - Computational Science and Mathematical Methods
                      (POF2-411) / 41G - Supercomputer Facility (POF2-41G21)},
      pid          = {G:(DE-HGF)POF2-411 / G:(DE-HGF)POF2-41G21},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-642-40047-6_83},
      url          = {https://juser.fz-juelich.de/record/138003},
}