Spatial　data　mining　is　the　process　of　discovering　interesting,　and　previously　unknown,　hut　potentially　useful　patterns　from　large　spatial　databases.　Extracting　interesting　and　useful　patterns　from　spatial　datasets　is　more　difficult　than　extracting　patterns　from　traditional　numeric　and　categorical　data　due　to　the　complexity　of　spatial　data　types,　spatial　relationships,　and　spatial　autocorrelation.　In　this　paper,　we　provide　a　new　visual　hierarchical　clustering　based　on　graph-partitioning　algorithm　called　VSG-CLUST,　which　groups　and　visualizes　cluster　hierarchies　consisting　of　both　non-spatial　and　spatial　attributes.　Our　method　is　fundamentally　different　from　conventional　clustering　algorithms,　that　usually　do　not　take　into　account　the　spatial　structure,　which　refers　to　the　distance　between　patterns,　topology,　density,　and　other　spatial　distribution　characteristics,　and　lack　efficient　level-of-detail　strategy　for　visualization.　In　contrast,　VSG-CLUST　is　able　to　recognize　spatial　patterns　that　involve　neighbors.　With　the　help　of　tree　graph　our　method　converts　a　multidimensional　spatial　clustering　problem　to　a　graph　partitioning　(tree　partitioning)　problem.　We　provide　a　theoretical　basis　for　the　approach　and　demonstrate　the　capability　of　the　graph　for　maintaining,　the　spatial　structure.　VSG-CLUST　is　implemented　in　a　fully　open　and　interactive　manner,　and　it　supports　various　visualization　techniques　including　data　mining　algorithm　visualization.　A　web-based　working　demo　with　Fujian　province　environmental　monitoring,　data　is　presented　to　illustrate　the　usability　and　effectiveness　of　VSG-CLUST　and　the　proposed　scheme.