Co-clustering　algorithms　have　been　widely　used　for　text　clustering　and　gene　expression　through　matrix　factorization.　In　recent　years,　diverse　co-clustering　algorithms　which　group　data　points　and　features　synchronously　have　shown　their　advantages　over　traditional　one-side　clustering.　In　order　to　solve　the　co-clustering　problems,　most　existing　methods　relaxed　constraints　via　matrix　factorization.　In　this　paper,　we　provide　a　detailed　understanding　of　six　co-clustering　algorithms　with　different　performance　and　robustness.　We　conduct　comprehensive　experiments　in　eight　real-world　datasets　to　compare　and　evaluate　these　co-clustering　methods　based　on　four　evaluation　metrics　including　clustering　accuracy,　normalized　mutual　information,　adjusted　rand　index,　and　purity.　Our　findings　demonstrate　the　strengths　and　weaknesses　of　these　methods　and　provide　insights　to　motivate　further　exploration　of　co-clustering　methods　and　matrix　factorization.