Incomplete　multi-view　clustering　is　an　important　and　challenging　task,　which　has　attracted　significant　attention　in　recent　years.　The　key　objective　of　incomplete　multi-view　clustering　is　to　excavate　the　underlying　avaliable　consistency　of　multi-view　data,　so　as　to　enable　the　effective　reconstruction　of　missing　views　for　clustering.　In　this　paper,　we　introduce　a　completion　framework　that　deeply　explores　the　underlying　consistency　and　effectively　completes　the　missing　views.　Following　that,　we　propose　a　novel　Twin　Reciprocal　Completion　for　Incomplete　multi-view　clustering,　termed　TRC-IMC　for　short.　To　be　specific,　TRC-IMC　jointly　conducts　the　Completion　in　Feature　space　(CF)　and　the　Completion　in　Subspace　(CS)　to　reciprocally　complete　the　data　with　missing　views.　The　underlying　high-order　consistency　of　multi-view　data　can　be　fully　explored　in　both　the　feature　space　and　subspace　to　guide　the　completion　process　of　missing　views.　Extensive　experiments　are　conducted　on　eight　real-world　multi-view　datasets,　and　experimental　results　indicate　the　promising　performance　of　our　method,　compared　to　several　state-of-the-arts.