With　the　widespread　deployment　of　sensors　and　the　Internet-of-Things,　multi-view　data　has　become　more　common　and　publicly　available.　Compared　to　traditional　data　that　describes　objects　from　single　perspective,　multi-view　data　is　semantically　richer,　more　useful,　however　more　complex.　Since　traditional　clustering　algorithms　cannot　handle　such　data,　multi-view　clustering　has　become　a　research　hotspot.　In　this　paper,　we　review　some　of　the　latest　multi-view　clustering　algorithms,　which　are　reasonably　divided　into　three　categories.　To　evaluate　their　performance,　we　perform　extensive　experiments　on　seven　real-world　data　sets.　Three　mainstream　metrics　are　used,　including　clustering　accuracy,　normalized　mutual　information　and　purity.　Based　on　the　experimental　results　and　a　large　number　of　literature　reading,　we　also　discuss　existing　problems　in　current　multi-view　clustering　and　point　out　possible　research　directions　in　the　future.　This　research　provides　some　insights　for　researchers　in　related　fields　and　may　further　promote　the　development　of　multi-view　clustering　algorithms.　©　2020　Elsevier　B.V.