  Consistency and complementarity are two key ingredients for boosting
multi-view clustering (MVC). Recently with the introduction of popular
contrastive learning, the consistency learning of views has been further
enhanced in MVC, leading to promising performance. However, by contrast, the
complementarity has not received sufficient attention except just in the
feature facet, where the Hilbert Schmidt Independence Criterion (HSIC) term or
the independent encoder-decoder network is usually adopted to capture
view-specific information. This motivates us to reconsider the complementarity
learning of views comprehensively from multiple facets including the feature-,
view-label- and contrast- facets, while maintaining the view consistency. We
empirically find that all the facets contribute to the complementarity
learning, especially the view-label facet, which is usually neglected by
existing methods. Based on this, we develop a novel \underline{M}ultifacet
\underline{C}omplementarity learning framework for
\underline{M}ulti-\underline{V}iew \underline{C}lustering (MCMVC), which fuses
multifacet complementarity information, especially explicitly embedding the
view-label information. To our best knowledge, it is the first time to use
view-labels explicitly to guide the complementarity learning of views. Compared
with the SOTA baseline, MCMVC achieves remarkable improvements, e.g., by
average margins over $5.00\%$ and $7.00\%$ respectively in complete and
incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.
