Attributed　graphs　have　attracted　much　attention　in　recent　years.　Different　from　conventional　graphs,　attributed　graphs　involve　two　different　types　of　heterogeneous　information,　i.e.,　structural　information,　which　represents　the　links　between　the　nodes,　and　attribute　information　on　each　of　the　nodes.　Clustering　on　attributed　graphs　usually　requires　the　fusion　of　both　types　of　information　in　order　to　identify　meaningful　clusters.　However,　most　of　existing　works　implement　the　combination　of　these　two　types　of　information　in　a　＂global＂　manner　by　treating　all　nodes　equally　and　learning　a　global　weight　for　the　information　fusion.　To　address　this　issue,　this　paper　proposed　a　novel　weighted　K-means　algorithm　with　＂local＂　learning　for　attributed　graph　clustering,　called　adaptive　fusion　of　structural　and　attribute　information　(Adapt-SA)　and　analyzed　the　convergence　property　of　the　algorithm.　The　key　advantage　of　this　model　is　to　automatically　balance　the　structural　connections　and　attribute　information　of　each　node　to　learn　a　fusion　weight,　and　get　densely　connected　clusters　with　high　attribute　semantic　similarity.　Experimental　study　of　weights　on　both　synthetic　and　real-world　data　sets　showed　that　the　weights　learned　by　Adapt-SA　were　reasonable,　and　they　reflected　which　one　of　these　two　types　of　information　was　more　important　to　decide　the　membership　of　a　node.　We　also　compared　Adapt-SA　with　the　state-of-the-art　algorithms　on　the　real-world　networks　with　varieties　of　characteristics.　The　experimental　results　demonstrated　that　our　method　outperformed　the　other　algorithms　in　partitioning　an　attributed　graph　into　a　community　structure　or　other　general　structures.