Multi-view　clustering,　which　explores　complementarity　and　consistency　among　multiple　distinct　feature　sets　to　boost　clustering　performance,　is　becoming　more　and　more　useful　in　many　real-world　applications.　Traditional　approaches　usually　map　multiple　views　to　a　unified　embedding,　in　which　some　weighted　mechanisms　are　utilized　to　measure　the　importance　of　each　view.　The　embedding,　serving　as　a　clustering　friendly　representation,　is　then　sent　to　extra　clustering　algorithms.　However,　a　unified　embedding　cannot　cover　both　complementarity　and　consistency　among　views　and　the　weighted　scheme　measuring　the　importance　of　each　view　as　a　whole　ignores　the　differences　of　features　in　each　view.　Moreover,　because　of　lacking　in　proper　grouping　structure　constraint　imposed　on　the　unified　embedding,　it　will　lead　to　just　multi-view　representation　learned,　which　is　not　clustering　friendly.　In　this　paper,　we　propose　a　novel　multi-view　clustering　method　to　alleviate　the　above　problems.　By　dividing　the　embedding　of　a　view　into　unified　and　view-specific　vectors　explicitly,　complementarity　and　consistency　can　be　reflected.　Besides,　an　adversarial　learning　process　is　developed　to　force　the　above　embeddings　to　be　non-trivial.　Then　a　fusion　strategy　is　automatically　learned,　which　will　adaptively　adjust　weights　for　all　the　features　in　each　view.　Finally,　a　Kullback-Liebler　(KL)　divergence　based　objective　is　developed　to　constrain　the　fused　embedding　for　clustering　friendly　representation　learning　and　to　conduct　clustering.　Extensive　experiments　have　been　conducted　on　various　datasets,　performing　better　than　the　state-of-the-art　clustering　approaches.