TESI DOCTORAL - La Salle

2.1. Related work on cluster ensembles
– weak clustering: another approach to the generation of homogeneous cluster ensembles
is the repeated application of computationally cheap and conceptually simple
clustering procedures that, although yielding poor clustering solutions by themselves
(this is why they are said to be weak),mayleadtobetterdataclusteringifcombined.
This type of strategies are of special interest when clustering high dimensional and/or
large data collections, as deriving multiple partitions by traditional means may become
too costly (Fern and Brodley, 2003). Examples of this include using random
hyperplanes for splitting the data (Topchy, Jain, and Punch, 2003) or prematurely
halted executions of k-means (Hadjitodorov and Kuncheva, 2007).
– noise injection: the random perturbation of the representation of the objects (Hadjitodorov,
Kuncheva, and Todorova, 2006) or the labels contained in the individual
clustering solutions (Hadjitodorov and Kuncheva, 2007) through noise injection has
also been applied in a few research works, although these strategies constitute a far
less natural way of creating diverse cluster ensembles if compared to the previous ones.
The second approach for creating cluster ensembles consists of applying several distinct
clustering algorithms for generating the individual components of the ensemble, which gives
rise to what are known as heterogeneous cluster ensembles. If clustering algorithms with
substantially different biases are employed, cluster ensembles with a high degree of diversity
can be obtained. This strategy has been applied in several works, such as (Strehl and
Ghosh, 2002; Lange and Buhmann, 2005; Gonzàlez and Turmo, 2006; Gionis, Mannila, and
Tsaparas, 2007; Gonzàlez and Turmo, 2008a; Gonzàlez and Turmo, 2008b).
Notice that the strategies used for creating homogeneous and heterogeneous cluster
ensembles can be combined so as to create even more diverse ensembles, as in (Sevillano et
al., 2007c), where a bunch of clustering algorithms are applied on different representations –
obtained by means of multiple feature extraction techniques with distinct dimensionalities–
of the objects in the data set. In this work, we will follow this approach as regards the
generation of cluster ensembles, using the clustering algorithms and object representations
described in appendices A.1 and A.3, as our goal is to overcome the indeterminacies resulting
from the selection of a particular clustering configuration.
There exist several recent works in the literature dealing with the design of the cluster
ensemble. In general terms, they can be divided into two categories: i) those works focused
on analyzing which strategies should be followed for creating cluster ensemble components
that give rise to good quality consensus clustering solutions, and ii) those centered on
obtaining a good quality consensus clustering given a particular cluster ensemble.
Among the first group, we highlight the works by Kuncheva and Hadjitorov. In (Hadjitodorov,
Kuncheva, and Todorova, 2006), the authors analyze the diversity of the individual
partitions composing a hard cluster ensemble and its effect on the quality of the
consensus clustering. To do so, several measures for evaluating the diversity of a cluster ensemble
are proposed and evaluated. Moreover, such measures are employed in the derivation
of a procedure for selecting the candidate with the median diversity among a population
of cluster ensembles, a criterion that leads to the obtention of equal or better consensus
clustering solutions than those obtained on arbitrarily chosen cluster ensembles.
The notion that moderately diverse cluster ensembles lead to good quality consensus
is reinforced by the experimental results presented in (Hadjitodorov and Kuncheva, 2007),
where the authors apply a standard genetic algorithm for driving the selection of the cluster
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