Maximum　margin　clustering　(MMC)　is　an　effective　clustering　algorithm,　which　first　extends　a　large　margin　principle　into　unsupervised　learning.　This　paper　revisits　the　MMC　problem　and　points　out　the　potential　problems　encountered　by　a　cutting　plane　approach.　We　propose　an　improved　MMC　algorithm　via　the　bundle　method　(BMMC).　Specifically,　the　constrained　convex-concave　procedure　algorithm　is　first　applied　to　decompose　the　MMC　problem　into　a　series　of　convex　sub-problems,　and　then,　the　bundle　method　is　adopted　to　efficiently　solve　each　sub-problem.　Moreover,　a　simpler　formulation　for　the　multi-class　MMC　is　presented.　In　addition　to　clustering　problems,　the　BMMC　is　also　extended　to　the　semi-supervised　case　by　incorporating　the　pairwise　constraints,　which　reveals　its　high　scalability.　Compared　with　the　previous　works,　the　proposed　solution　is　much　simpler　and　faster.　The　experiments　on　several　data　sets　are　conducted　to　demonstrate　the　effectiveness　of　our　proposed　algorithm.