The　goal　of　mixed-modality　clustering,　which　differs　from　typical　multi-modality/view　clustering,　is　to　divide　samples　derived　from　various　modalities　into　several　clusters.　This　task　has　to　solve　two　critical　semantic　gap　problems:　i)　how　to　generate　the　missing　modalities　without　the　pairwise-modality　data;　and　ii)　how　to　align　the　representations　of　heterogeneous　modalities.　To　tackle　the　above　problems,　this　paper　proposes　a　novel　mixed-modality　clustering　model,　which　integrates　the　missing-modality　generation　and　the　heterogeneous　modality　alignment　into　a　unified　framework.　During　the　missing-modality　generation　process,　a　bidirectional　mapping　is　established　between　different　modalities,　enabling　generation　of　preliminary　representations　for　the　missing-modality　using　information　from　another　modality.　Then　the　intra-modality　bipartite　graphs　are　constructed　to　help　generate　better　missing-modality　representations　by　weighted　aggregating　existing　intra-modality　neighbors.　In　this　way,　a　pairwise-modality　representation　for　each　sample　can　be　obtained.　In　the　process　of　heterogeneous　modality　alignment,　each　modality　is　modelled　as　a　graph　to　capture　the　global　structure　among　intra-modality　samples　and　is　aligned　against　the　heterogeneous　modality　representations　through　the　adaptive　heterogeneous　graph　matching　module.　Experimental　results　on　three　public　datasets　show　the　effectiveness　of　the　proposed　model　compared　to　multiple　state-of-the-art　multi-modality/view　clustering　methods.