TESI DOCTORAL - La Salle

5.1. Generation of multimodal cluster ensembles
X 1
Object
representation p
+ clustering
Object
X2 representation
X
+ clustering
Multimedia
data set
Xm Object
representation
+ clustering
E
Consensus
architecture
(fl (flat t or
hierarchical)
Feature
fusion
Object
representation
+ clustering
Multimodal cluster ensemble generation
c
Consensus
self
refining
Figure 5.1: Block diagram of the proposed multimodal consensus clustering system.
we can take advantage of both modality fusion approaches, which can be of help to reveal
the group structure of the data.
The proposed multimodal consensus clustering approach follows the schematic block
diagram of figure 5.1, and consists of the following steps: the generation of the multimodal
cluster ensemble E, plus the application of a computationally efficient consensus architecture
that, followed by a consensus-based self-refining procedure, yields the final partition of the
multimodal data collection subject to clustering, λfinal c .
In this chapter, the phases that constitute the multimodal consensus clustering process
are described and contextualized in the framework of the experiments conducted in this
work. For starters, section 5.1 presents the strategies followed in the creation of the multimodal
cluster ensemble. Next, section 5.2 describes the particularities of the consensus
architecture and the self-refining procedure that give rise to the multimodal data partition.
Last, the results of the multimedia consensus clustering experiments run in this thesis
are presented in section 5.3, and with the conclusions discussed in section 5.4 the present
chapter comes to an end.
5.1 Generation of multimodal cluster ensembles
The key point for conducting a multimodal consensus clustering process lies in the creation
of a cluster ensemble that contains both clusterings derived on each data modality separately
and on fused modalities. In this section, we describe a general procedure for creating a
multimodal cluster ensemble E upon a multimedia data collection.
Without loss of generality, let us assume that the multimodal data collection subject to
clustering contains n objects represented by numeric attributes. Thus, the whole data set
can be formally represented by means of a d × n real-valued matrix X, where each object
is represented by means of a d-dimensional column vector xi, ∀i ∈ [1,n].
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final
c