  It is still challenging to cluster multi-view data since existing methods can
only assign an object to a specific (singleton) cluster when combining
different view information. As a result, it fails to characterize imprecision
of objects in overlapping regions of different clusters, thus leading to a high
risk of errors. In this paper, we thereby want to answer the question: how to
characterize imprecision in multi-view clustering? Correspondingly, we propose
a multi-view low-rank evidential c-means based on entropy constraint (MvLRECM).
The proposed MvLRECM can be considered as a multi-view version of evidential
c-means based on the theory of belief functions. In MvLRECM, each object is
allowed to belong to different clusters with various degrees of support (masses
of belief) to characterize uncertainty when decision-making. Moreover, if an
object is in the overlapping region of several singleton clusters, it can be
assigned to a meta-cluster, defined as the union of these singleton clusters,
to characterize the local imprecision in the result. In addition,
entropy-weighting and low-rank constraints are employed to reduce imprecision
and improve accuracy. Compared to state-of-the-art methods, the effectiveness
of MvLRECM is demonstrated based on several toy and UCI real datasets.
