% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Frber:560970,
author = {Färber, Ines},
othercontributors = {Seidl, Thomas and Assent, Ira},
title = {{A}lternative clustering in subspace projections; 1.
{A}ufl.},
volume = {6},
school = {Zugl.: Aachen, Techn. Hochsch.},
type = {Dissertation},
address = {Aachen},
publisher = {Apprimus-Verl.},
reportid = {RWTH-2015-06688},
isbn = {978-3-86359-368-1},
series = {Ergebnisse aus der Informatik},
year = {2015},
note = {Weitere Reihe: Edition Wissenschaft Apprimus. - Auch
veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2016; Zugl.: Aachen, Techn. Hochsch., Diss.,
2014},
abstract = {The technological advancements of recent years led to a
pervasion of all life areas with information systems and
allows to conveniently and affordably gather large amounts
of data. The key to our information society is the
transformation of the mere data in these comprehensive
databases into information and knowledge. One research area
committed to this goal is the one of data mining, where the
task is to automatically or semi-automatically extract
previously unknown patterns from such data sources. The
subject of this thesis is the mining task of clustering,
which aims at grouping objects based on their similarity
such that similar objects are grouped together, while
dissimilar ones are separated. Since modern storage systems
are not subject to practical limitations anymore, data can
be captured in its full complexity without restriction to a
small selective set of aspects. For such complex data, just
identifying a single clustering is often not sufficient.
Instead, multiple, alternative, and valid clusterings can be
identified for a single dataset, each highlighting different
aspects of the data. The paradigm of multi-view clustering,
also referred to as alternative clustering, is dedicated to
explicitly discover such a diverse set of multiple,
alternative clusterings in order to find all hidden patterns
in the data. A second observation for complex data sources,
where usually many characteristics are stored for each
object, is the inability to find similar objects by
considering all of these characteristics. While clustering
based on all attributes, in the full-space, is futile,
valuable cluster patterns can be found for subsets of
attributes, in subspace projections. This problem is tackled
by approaches of the subspace clustering paradigm, which aim
at uncovering clustering structures hidden in subspace
projections, such that for each cluster a set of relevant
attributes is determined automatically. In this thesis, we
want to highlight fundamental parallels between the two
paradigms of multi-view clustering and subspace clustering,
since both account for the possibility of objects belonging
to multiple clusters simultaneously. Consequently, we
present several approaches exploiting synergy effects by
combining both paradigms to find multiple, alternative
clusterings in subspace projections of the data.},
cin = {122510 / 120000},
ddc = {004},
cid = {$I:(DE-82)122510_20140620$ / $I:(DE-82)120000_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
urn = {urn:nbn:de:hbz:82-rwth-2015-066881},
url = {https://publications.rwth-aachen.de/record/560970},
}
guest ::