  The rapid emergence of high-dimensional data in various areas has brought new
challenges to current ensemble clustering research. To deal with the curse of
dimensionality, recently considerable efforts in ensemble clustering have been
made by means of different subspace-based techniques. However, besides the
emphasis on subspaces, rather limited attention has been paid to the potential
diversity in similarity/dissimilarity metrics. It remains a surprisingly open
problem in ensemble clustering how to create and aggregate a large population
of diversified metrics, and furthermore, how to jointly investigate the
multi-level diversity in the large populations of metrics, subspaces, and
clusters in a unified framework. To tackle this problem, this paper proposes a
novel multidiversified ensemble clustering approach. In particular, we create a
large number of diversified metrics by randomizing a scaled exponential
similarity kernel, which are then coupled with random subspaces to form a large
set of metric-subspace pairs. Based on the similarity matrices derived from
these metric-subspace pairs, an ensemble of diversified base clusterings can
thereby be constructed. Further, an entropy-based criterion is utilized to
explore the cluster-wise diversity in ensembles, based on which three specific
ensemble clustering algorithms are presented by incorporating three types of
consensus functions. Extensive experiments are conducted on 30 high-dimensional
datasets, including 18 cancer gene expression datasets and 12 image/speech
datasets, which demonstrate the superiority of our algorithms over the
state-of-the-art. The source code is available at
https://github.com/huangdonghere/MDEC.
