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@PHDTHESIS{Mller:63728,
      author       = {Müller, Emmanuel Alexander},
      othercontributors = {Seidl, Thomas},
      title        = {{E}fficient knowledge discovery in subspaces of high
                      dimensional databases},
      address      = {Aachen},
      publisher    = {Publikationsserver der RWTH Aachen University},
      reportid     = {RWTH-CONV-125151},
      pages        = {270 S. : graph. Darst.},
      year         = {2010},
      note         = {Zsfassung in engl. und dt. Sprache; Aachen, Techn.
                      Hochsch., Diss., 2010},
      abstract     = {In many recent applications such as sensor network
                      analysis, customer segmentation or gene expression analysis
                      tremendous amount of data is gathered. As collecting and
                      storing of data is cheap, users tend to record everything
                      they can. Thus, in today's applications for each object one
                      uses many attributes to provide as much information as
                      possible. However, the valuable knowledge to be learned out
                      of this information is hidden in subsets of the given
                      attributes. Considering any of these subspaces one expands
                      the search space significantly. This poses novel challenges
                      to data mining techniques which aim at extracting this
                      knowledge out of high dimensional databases. This work has
                      its focus on clustering as one of the main data mining
                      tasks. Clustering is an established technique for grouping
                      objects based on mutual similarity. As traditional
                      clustering approaches are unable to detect clusters hidden
                      in subspaces of high dimensional databases, recent subspace
                      clustering models have been proposed that detect groups of
                      similar objects in any subset of the given attributes.
                      However, as the number of possible subspaces scales
                      exponentially with the number of attributes, development of
                      efficient techniques is crucial for knowledge discovery in
                      subspaces of high dimensional databases. In this work we
                      propose both novel subspace clustering models aiming at high
                      quality results and efficient processing schemes for these
                      models. We start with novel subspace cluster definitions
                      ensuring the detection of clusters in arbitrary subspaces.
                      We highlight the general challenges of redundancy in recent
                      subspace clustering models and propose novel non-redundant
                      subspace clustering definitions. In this context, our aim is
                      to reduce result sizes to all and only novel knowledge by
                      optimizing the overall subspace clustering result. According
                      to these models not all subspace clusters are valuable for
                      the final result. Based on this general observation we
                      propose efficient processing schemes. Our novel algorithmic
                      solutions overcome efficiency problems caused by exhaustive
                      search of almost all subspace projections and costly
                      database access. We select only the most promising subspace
                      regions for efficient subspace clustering. Overall, our
                      techniques are scalable to large and high dimensional
                      databases providing only few but high quality subspace
                      clusters. Furthermore, as a general contribution to the
                      community we provide a systematic evaluation study on a
                      broad set of approaches. We show both efficiency and quality
                      characteristics of major paradigms. As major aspect for
                      sustained scientific research we ensure repeatability and
                      comparability for all of our empirical results. Our
                      evaluation framework is available as open source project and
                      provides a basis for future enhancements in this research
                      area. Thus, this thesis provides not only novel methods for
                      efficient cluster and also outlier detection in subspaces of
                      high dimensional data, but it is a fundamental basis for
                      repeatable comparison of recent data mining approaches.},
      keywords     = {Data Mining (SWD) / Wissensextraktion (SWD) /
                      Cluster-Analyse (SWD) / Dichtebasiertes Clusterverfahren
                      (SWD) / Ausreißer <Statistik> (SWD) / Evaluation (SWD) /
                      Open Source (SWD)},
      cin          = {122510 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)122510_20140620$ / $I:(DE-82)120000_20140620$},
      shelfmark    = {H.2.8},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:82-opus-33895},
      url          = {https://publications.rwth-aachen.de/record/63728},
}