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6 RELATED WORK
In the area of clustering Web users, it is a new ap-
proach to take into account the non-obvious profiles,
described in (Zicari et al., 2006). Clustering non-
obvious-profiles with CORD takes indirectly into ac-
count the time spent by the user on a page and the con-
tent topics of this page. CORD clusters users based
on their supposed interest in these topics. With the
fuzzy centroids, it gives an interpretation to the clus-
ters as a value of a predefined scale of interest. This is
a nameable advantage. Several algorithms have been
proposed in the area of clustering large datasets, as
BIRCH (Zhang et al., 1996) and CLARANS (Cluster-
ing Large Applications based on RANdom Search) by
Ng and Han (Ng and Han, 1994). There are special-
ized fields, e.g. multi-relational data clustering. Yin
and Han proposed here CrossClus (Yin et al., 2007),
that clusters data stored in multiple relational tables
based on user guidance and multi-relational features.
This algorithm requires as CORD the help of the
user, that is here the person who wants to cluster
the elements. Clustering can be applied to various
domains and issues, e.g. in (Aggarwal et al., 2006)
the k-anonymity (a technique to preserve privacy in
data) is treated as a special clustering problem, called
r-cellular clustering. (Aggarwal et al., 2006) han-
dle categorical attributes by the representation as n
equidistant points in a metric space. Hybrid Systems
are used in various research fields, e.g. in the area of
Web Burke (Burke, 2002) has defined Hybrid Recom-
mender Systems, that combine information filtering
and collaborative filtering techniques. Helmer pro-
posed in (Helmer, 2007) a hybrid approach to mea-
sure the similarity of semistructured documents based
on entropy. (Kossmann et al., 2002) use a hybrid
approach to find the Skyline, i.e. a set of interesting
points from a potentially large set of data.
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