Many　clustering　methods　have　been　developed　to　identify　functional　modules　in　Protein-Protein　Interaction　(PPI)　networks　but　the　results　are　far　from　satisfaction.　To　overcome　the　noise　and　incomplete　problems　of　PPI　networks　and　find　more　accurate　and　stable　functional　modules,　we　propose　an　integrative　method,　bipartite　graph-based　Non-negative　Matrix　Factorisation　method　(BiNMF),　in　which　we　adopt　multiple　biological　data　sources　as　different　views　that　describe　PPIs.　Specifically,　traditional　clustering　models　are　adopted　as　preliminary　analysis　of　different　views　of　protein　functional　similarity.　Then　the　intermediate　clustering　results　are　represented　by　a　bipartite　graph　which　can　comprehensively　represent　the　relationships　between　proteins　and　intermediate　clusters　and　finally　overlapping　clustering　results　are　achieved.　Through　extensive　experiments,　we　see　that　our　method　is　superior　to　baseline　methods　and　detailed　analysis　has　demonstrated　the　benefits　of　integrating　diverse　clustering　methods　and　multiple　biological　information　sources.