TESI DOCTORAL - La Salle

1.5. Motivation and contributions of the thesis
Obj Object
representation
Multimedia
data set R df D df df ,
X Clustering E
df df A
( hard / soft)
Flat/
hi hierarchical hi l
(serial/parallel)
consensus architecture
( hard / soft)
c
or
c
Consensus
final
c
self- or
refining final
c
Figure 1.6: Block diagram of the robust multimodal clustering system based on self-refining
hierarchical consensus architectures.
– the construction of multimodal cluster ensembles and the application of self-refining
hierarchical consensus architectures for robust multimodal clustering (see chapter 5).
– consensus functions based on voting strategies for combining fuzzy partitions contained
in soft cluster ensembles —see chapter 6.
These contributions can be articulated in a unitary proposal for robust multimodal
clustering based on cluster ensembles, a block diagram of which is shown in figure 1.6. The
procedure for deriving the partition of a multimodal data collection X accordingtoour
proposal goes as follows: firstly, multiple representations of the objects contained in X are
created by the application of a set of representational and dimensional diversity factors
provided by the user (denoted as dfR and dfD in figure 1.6). Next, a set of either hard or
soft clustering algorithms (referred to as the algorithmic diversity factor dfA) are applied on
the distinct object representations obtained from the previous step, giving rise to a set of
clusterings compiled in the cluster ensemble E. Notice that, up to this point, the only choices
made by the user refer to the object representation techniques and clustering algorithms
employed for creating the ensemble. As mentioned earlier, the user is encouraged to employ
the widest possible range of diversity factors, thus creating maximally diverse clusterings
so as to break free from the indeterminacies inherent to clustering. The obviously high
computational cost associated to this cluster ensemble generation strategy can somehow be
mitigated considering it is a highly parallelizable process (Hore, Hall, and Goldgof, 2006).
Subsequently, the process for deriving the partition of the data set X upon the cluster
ensemble E starts by applying a consensus clustering procedure. This can either be
conducted according to a flat or a hierarchical consensus architecture, a decision that is
automatically made by the system based on the characteristics of the data set X, the cluster
ensemble E and the consensus function F employed for combining the clusterings in E
—which is selected by the user. In case a hierarchical consensus architecture is employed,
an additional decision (also made with no user supervision) is the one related to its serial or
parallel execution, which ultimately depends on the availability of computational resources.
As a result, a consensus clustering solution is obtained, which can either be represented
by a consensus label vector λc or a consensus clustering matrix Λc, depending on whether
a crisp or a fuzzy clustering approach is taken. Subsequently, this consensus clustering is
subjected to an almost fully autonomous self-refining procedure, which requires the user
to specify a percentage threshold (denoted by symbol ‘%’ in figure 1.6). Finally, the final
partition of the data set X is obtained, denoted as λ final
c (or Λfinal c in the fuzzy case).
Before proceeding with the description of our proposals, the next chapter presents an
overview of related work in the area of cluster ensembles.
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