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Chapter 3. Hierarchical consensus architectures
yield lower quality consensus clustering solutions when compared to the other consensus
functions.
Thus, in some sense, we face a further indeterminacy, this one referred to the consensus
function to apply. However, this indeterminacy can be overcome by taking advantage of the
capability of creating several consensus clustering solutions by means of multiple consensus
functions in computationally optimal time, and subsequently, apply a supraconsensus
function that allows selecting the highest quality consensus clustering solution in a fully
unsupervised manner, as proposed in (Strehl and Ghosh, 2002).
Besides this use, supraconsensus strategies constitute a basic ingredient of the consensus
self-refining procedure presented in the next chapter, which is oriented to better the quality
of consensus clustering solutions as a means for creating robust clustering systems upon
consensus clustering processes.
3.6 Related publications
Our first approach to hierarchical consensus architectures dealt with deterministic HCA
(Sevillano et al., 2007a), although it was solely focused on the analysis of the quality of
the consensus clusterings obtained, not on its computational aspect. The details of this
publication, presented as a poster at the ECIR 2007 conference held at Rome, are described
next.
Authors: Xavier Sevillano, Germán Cobo, Francesc Alías and Joan Claudi Socoró
Title: A Hierarchical Consensus Architecture for Robust Document Clustering
In: Proceedings of 29th European Conference on Information Retrieval (ECIR 2007)
Publisher: Springer
Series: Lecture Notes in Computer Science
Volume: 4425
Editors: Giambattista Amati, Claudio Carpineto and Giovanni Romano
Pages: 741-744
Year: 2007
Abstract: A major problem encountered by text clustering practitioners is the difficulty
of determining aprioriwhich is the optimal text representation and clustering
technique for a given clustering problem. As a step towards building robust document
partitioning systems, we present a strategy based on a hierarchical consensus clustering
architecture that operates on a wide diversity of document representations and
partitions. The conducted experiments show that the proposed method is capable of
yielding a consensus clustering that is comparable to the best individual clustering
available even in the presence of a large number of poor individual labelings, outperforming
classic non-hierarchical consensus approaches in terms of performance and
computational cost.
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