TESI DOCTORAL - La Salle

A.1. The CLUTO clustering package
following paragraphs:
1. direct: this method computes the desired k-way clustering solution by simultaneously
finding all k clusters.
2. rb: repeated-bisecting clustering process, in which the desired k-way clustering solution
is computed by performing a sequence of k −1 repeated bisections of the data set.
At each bisecting step, one of the obtained clusters is selected and bisected further, so
that each partial 2-way clustering solution optimizes the selected clustering criterion
function locally.
3. rbr: a refined repeated-bisecting method that performs a global optimization of the
clustering solution obtained by the rb algorithm.
4. agglo: agglomerative hierarchical clustering that locally optimizes the selected criterion
function, stopping the agglomeration process when k clusters are obtained.
5. bagglo: biased agglomerative clustering, which applies the agglo clustering method
on an augmented representation of the objects created by concatenating the d original
attributes of each object and √ n new features which are proportional to the
similarity between that object and its cluster centroid according to a √ n-way partitional
clustering solution that is initially computed on the data set by means of the
rb algorithm.
6. graph: graph-based clustering, in which the data set is modelled as a nearest-neighbor
graph (each object is a vertex connected to the vertices representing its most similar
objects) that is partitioned into k clusters according to one of the graph criterion
functions.
An enumeration of the eleven criterion functions implemented in the CLUTO software
package follows (Zhao and Karypis, 2001):
a. i1 : internal criterion function that maximizes the sum of the average pairwise similarities
between the objects assigned to each cluster, weighted according to its size. Its
maximization is equivalent to minimize sum-of-squared-distances between the objects
in the same cluster, as in traditional k-means (Zhao and Karypis, 2001).
b. i2 : internal criterion function that maximizes the similarity between each object and
the centroid of the cluster it is assigned to.
c. e1 : external criterion function that minimizes the proximity between each cluster’s
centroid and the common centroid of the rest of the data set.
d. h1 : hybrid criterion function that simultaneosly maximizes i1 and minimizes e1.
e. g1 : MinMaxCut criterion function applied on the graph obtained by computing pairwise
object similarities, partitioning the objects into groups by minimizing the edgecut
of each partition (for graph-based clustering only).
f. g1p: normalized Cut criterion function applied on the graph obtained by viewing the
objects and their features as a bipartite graph, simultaneously partitioning the objects
and their features (for graph-based clustering only).
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