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Appendix A
Experimental setup
A.1 The CLUTO clustering package
All the clustering algorithms employed in the experimental sections of this work have been
extracted from the CLUTO clustering toolkit. In its authors’ words, “CLUTO is a software
package for clustering low- and high-dimensional data sets and for analyzing the characteristics
of the obtained clusters. CLUTO is well-suited for clustering data sets arising in many
diverse application areas including information retrieval, customer purchasing transactions,
web, geographic information systems, science, and biology”. It is available online for download
at http://glaros.dtc.umn.edu/gkhome/cluto/cluto/download. We chose CLUTO as
our clustering algorithm provider due to its ease of use, robustness, speed and scalability,
as CLUTO’s algorithms have been optimized for operating on very large data sets both
in terms of the number of objects (up to ∼ 10 5 ) as well as the number of features (up to
∼ 10 4 ).
CLUTO provides clustering algorithms based on the partitional, agglomerative, and
graph-partitioning paradigms. Most of the algorithms implemented in CLUTO treat clustering
as an optimization problem, thus seeking to maximize or minimize a particular clustering
criterion function, which can be defined either globally or locally over the entire
clustering solution space. As in any clustering process, computing the value of these criterion
functions requires measuring the similarity between the objects in the data set. This
means that, for applying a specific CLUTO clustering algorithm, it is necessary to select
the desired:
– clustering strategy (clustering paradigm and specific implementation): CLUTO includes
six implementations of partitional, hierarchical agglomerative and graph-based
clustering strategies.
– criterion function: CLUTO provides a total of eleven criterion functions for driving
its clustering algorithms.
– similarity measure: CLUTO allows to measure the similarity between objects using
four distinct alternatives.
The six implementations of partitional, hierarchical agglomerative and graph-based clustering
strategies available in the CLUTO clustering toolkit are briefly described in the
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