Traditional　multiview　clustering　relies　on　manually-designed　optimization　problems　based　on　prior　interpretable　knowledge　to　cluster　objects　with　similar　attributes,　but　it　may　be　hindered　by　limited　feature　extraction　capability.　In　contrast,　deep　multiview　clustering　overcomes　this　limitation　by　utilizing　learning-based　nonlinear　transformations　for　clustering,　but　it　may　be　restricted　by　model　interpretability　caused　by　blackbox　networks.　Besides,　previous　multiview　clustering　methods　only　consider　homogeneous　or　heterogeneous　situations,　resulting　in　restricted　scalability　and　generalizability.　To　address　the　aforementioned　issues,　we　design　a　universal　interpretable　clustering　framework　that　accommodates　both　homogeneous　and　heterogeneous　multiview　scenarios.　To　realize　this　purpose:　1)　we　revisit　the　interpretable　knowledge-driven　design　architecture　of　traditional　multiview　methods　and　formulate　clustering　optimization　problems　on　multiview　data　attributes　in　homogeneous　scenarios;　2)　the　optimization　problem　is　leveraged　to　derive　network　modules　that　learn　shared　and　self-expressive　representations　for　clustering,　with　the　practical　meaning　of　each　network　component　providing　model　design-level　interpretability;　3)　the　proposed　method　is　extended　from　homogeneous　to　heterogeneous　scenarios,　enhancing　its　universality　for　a　broader　spectrum　of　multiview　clustering　tasks;　and　4)　tailored　training　loss　for　the　clustering　task　is　employed　to　inversely　enhance　the　affinity　between　objects　with　similar　attributes.　Extensive　experimental　results　on　both　homogeneous　and　heterogeneous　multiview　datasets　demonstrate　the　superior　effectiveness　and　adaptability　of　the　proposed　framework　compared　to　state-of-the-art　clustering　methods.