Spatial　clustering　has　been　an　active　research　area　in　the　data　mining　community.　Spatial　clustering　is　not　only　an　important　effective　method　but　also　a　prelude　of　other　task　for　Spatial　Data　Mining　(SDM).　In　this　paper,　we　propose　an　Improved　Ant　Colony　Optimization　(IACO)　and　Quantum　Particle　Swarm　Optimization　(QPSO)　method　for　Spatial　Clustering　with　Obstacles　Constraints　(SCOC).　In　the　process　of　doing　so,　we　first　use　IACO　to　obtain　the　shortest　obstructed　distance,　and　then　we　develop　a　novel　QPKSCOC　based　on　QPSO　and　K-Medoids　to　cluster　spatial　data　with　obstacles.　The　experimental　results　demonstrate　that　the　proposed　method,　performs　better　than　Improved　K-Medoids　SCOC　in　terms　of　quantization　error　and　has　higher　constringency　speed　than　Genetic　K-Medoids　SCOC.　©　2009　IEEE.