  Ensemble clustering has been a popular research topic in data mining and
machine learning. Despite its significant progress in recent years, there are
still two challenging issues in the current ensemble clustering research.
First, most of the existing algorithms tend to investigate the ensemble
information at the object-level, yet often lack the ability to explore the rich
information at higher levels of granularity. Second, they mostly focus on the
direct connections (e.g., direct intersection or pair-wise co-occurrence) in
the multiple base clusterings, but generally neglect the multi-scale indirect
relationship hidden in them. To address these two issues, this paper presents a
novel ensemble clustering approach based on fast propagation of cluster-wise
similarities via random walks. We first construct a cluster similarity graph
with the base clusters treated as graph nodes and the cluster-wise Jaccard
coefficient exploited to compute the initial edge weights. Upon the constructed
graph, a transition probability matrix is defined, based on which the random
walk process is conducted to propagate the graph structural information.
Specifically, by investigating the propagating trajectories starting from
different nodes, a new cluster-wise similarity matrix can be derived by
considering the trajectory relationship. Then, the newly obtained cluster-wise
similarity matrix is mapped from the cluster-level to the object-level to
achieve an enhanced co-association (ECA) matrix, which is able to
simultaneously capture the object-wise co-occurrence relationship as well as
the multi-scale cluster-wise relationship in ensembles. Finally, two novel
consensus functions are proposed to obtain the consensus clustering result.
Extensive experiments on a variety of real-world datasets have demonstrated the
effectiveness and efficiency of our approach.
