In　this　paper,　a　spectral　clustering　ensemble　algorithm　based　on　synthetic　similarity　(SCEBSS)　is　proposed　to　improve　the　performance　of　clustering.　Multiple　methods　of　vector　similarity　measurement　are　adopted　to　produce　diverse　similarity　matrices　of　objects.　Every　similarity　matrix　is　given　a　weight　and　then　added　as　a　synthetic　similarity　matrix.　A　spectral　clustering　algorithm　is　employed　on　the　synthetic　similarity　matrix,　and　then　a　particle　swarm　optimization　using　normalized　mutual　information　(NMI)　as　evaluation　function　is　adopted　to　optimize　the　weights　of　similarity　matrices　to　obtain　the　best　clusters.　Comparisons　with　other　related　clustering　schemes　demonstrate　the　better　performance　of　SCEBSS　in　clustering　data　tasks　and　robustness　to　noise.　©　2011　IEEE.