  Incomplete multi-view clustering is an important technique to deal with
real-world incomplete multi-view data. Previous works assume that all views
have the same incompleteness, i.e., balanced incompleteness. However, different
views often have distinct incompleteness, i.e., unbalanced incompleteness,
which results in strong views (low-incompleteness views) and weak views
(high-incompleteness views). The unbalanced incompleteness prevents us from
directly using the previous methods for clustering. In this paper, inspired by
the effective biological evolution theory, we design the novel scheme of view
evolution to cluster strong and weak views. Moreover, we propose an Unbalanced
Incomplete Multi-view Clustering method (UIMC), which is the first effective
method based on view evolution for unbalanced incomplete multi-view clustering.
Compared with previous methods, UIMC has two unique advantages: 1) it proposes
weighted multi-view subspace clustering to integrate these unbalanced
incomplete views, which effectively solves the unbalanced incomplete multi-view
problem; 2) it designs the low-rank and robust representation to recover the
data, which diminishes the impact of the incompleteness and noises. Extensive
experimental results demonstrate that UIMC improves the clustering performance
by up to 40% on three evaluation metrics over other state-of-the-art methods.
