TESI DOCTORAL - La Salle

Appendix A. Experimental setup
g. slink: traditional single-link criterion function (for agglomerative hierarchical clustering
only).
h. wslink: cluster-weighted single-link criterion function (for agglomerative hierarchical
clustering only).
i. clink: traditional complete-link criterion function (for agglomerative hierarchical clustering
only).
j. wclink: cluster-weighted complete-link criterion function (for agglomerative hierarchical
clustering only).
k. upgma: traditional unweighted pair-group method with arithmetic means criterion
function (for agglomerative hierarchical clustering only).
Finally, as regards the similarity measures that can be employed by the clustering algorithms
implemented in CLUTO, they are described next (Zhao and Karypis, 2001):
i. cos: the similarity between objects is computed using the cosine function.
ii. corr : the similarity between objects is computed using the correlation coefficient.
iii. dist: the similarity between objects is computed to be inversely proportional to the
Euclidean distance (for graph-based clustering only).
iv. jacc: the similarity between objects is computed using the extended Jaccard coefficient
(for graph-based clustering only).
For further insight on the distinct implementations of the clustering strategies, formal
definitions of the criterion functions and similarity measures, or the criterion functions
optimization procedure, the interested reader is referred to (Zhao and Karypis, 2001; Zhao
and Karypis, 2003b).
As the reader may infer from the previous enumerations, not all the clustering strategycriterion
function-similarity measure combinations are possible in CLUTO. Table A.1 presents
which triplets are allowed (denoted by the symbol), which are not allowed (denoted
by ×), and which have been employed in our experiments (denoted by •)—28 out of the 68
combinations allowed by CLUTO.
In the experiments, each specific algorithm is identified by the clustering strategysimilarity
measure-criterion function triplet employed, e.g. agglo-cos-slink (agglomerative
clustering using the single link criterion and measuring object proximity with the cosine
similarity), graph-jacc-i2 (graph-based clustering using the internal criterion function #2
and the Jaccard distance), etc.
A.2 Data sets
In this work we have applied clustering processes on a total of sixteen data sets, 12 unimodal
and four multimodal. In this section, we present their main features, such as their origin,
the number of objects they contain (denoted throughout this work by n), the number (d)
and meaning of their attributes, and the expected number of categories (k).
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