The　surge　in　data　with　multiple　views　has　propelled　significant　interest　in　the　domain　of　multi-view　clustering.　Unlike　conventional　single-view　data,　multi-view　data　offers　a　more　accurate　representation　of　objects.　However,　the　pivotal　challenge　remains　in　the　effective　categorization　of　data　through　feature　extraction　from　multiple　views　within　clustering　tasks.　Notably,　prevailing　multi-view　clustering　algorithms　often　emphasize　the　derivation　of　appropriate　view　weights,　inadvertently　sidestepping　optimization　intricacies.　This　approach　frequently　leads　to　protracted　computational　time　due　to　the　resource-intensive　matrix　multiplication　operations　involved　in　optimization,　coupled　with　the　necessity　of　weight　allocation　for　diverse　views.　Addressing　this,　we　propose　an　optimization　framework　founded　on　the　optimal　transport　algorithm　that　operates　independently　of　view　weights.　A　paramount　advantage　of　the　optimal　transport　algorithm　lies　in　its　rapid　convergence　to　a　closed-form　solution.　This　study　diverges　from　the　conventional　focus　on　view　weights　and　centers　on　the　optimization　process　within　clustering　algorithms　for　resolving　multi-view　challenges.　The　introduced　framework　employing　the　optimal　transport　algorithm　significantly　mitigates　computational　complexity　while　handling　multiple　views.　Rigorous　experimentation　duly　substantiates　the　efficacy　of　the　proposed　framework　in　the　realm　of　multi-view　clustering.　Across　publicly　available　multi-view　datasets,　our　framework　exhibits　superior　performance　over　existing　state-of-the-art　algorithms.　©　2023　Elsevier　B.V.