Data　association　is　the　foundation　of　state　estimation　in　mobile　robot　simultaneous　localization　and　mapping.　Aiming　at　the　problems　of　false　association,　high　computational　complexity　in　joint　compatible　branch　and　bound　algorithm,　we　propose　an　optimized　joint　compatible　branch　and　bound　data　association　algorithm　based　on　Gaussian　mixture　clustering.　Firstly,　the　local　association　strategy　is　adopted　to　limit　data　association　in　local　region,　so　as　to　reduce　the　number　of　features　involved　in　data　association　at　the　current　moment.　Secondly,　the　Gaussian　mixture　clustering　algorithm　is　used　in　local　areas　to　group　the　observed　values　at　the　current　moment,　so　as　to　get　several　groups　that　have　little　correlation　with　each　other.　Finally,　joint　compatible　branch　and　bound　data　association　algorithm　is　used　in　each　group　for　data　association,　and　the　optimal　solution　is　obtained　according　to　mutual　exclusion　criteria　and　optimal　criteria.　The　experiment　results　verify　that　the　algorithm　improved　the　accuracy　of　data　association,　reduced　the　computational　complexity　and　improved　the　efficiency　of　data　association.