In　order　to　cluster　the　textual　data　with　high　dimension　in　modern　data　analysis,　the　spherical　k-means　clustering　is　presented.　It　aims　to　partition　the　given　points　with　unit　length　into　k　sets　so　as　to　minimize　the　within-cluster　sum　of　cosine　dissimilarity.　In　this　paper,　we　mainly　study　seeding　algorithms　for　spherical　k-means　clustering,　for　its　special　case　(with　separable　sets),　as　well　as　for　its　generalized　problem　(alpha　spherical　k-means　clustering).　About　the　spherical　k-means　clustering　with　separable　sets,　an　approximate　algorithm　with　a　constant　factor　is　presented.　Moreover,　it　can　be　generalized　to　the　alpha-spherical　separable　k-means　clustering.　By　slickly　constructing　a　useful　function,　we　also　show　that　the　famous　seeding　algorithms　such　as　k-means++　and　k-means||　for　k-means　problem　can　be　applied　directly　to　solve　the　alpha-spherical　k-means　clustering.　Except　for　theoretical　analysis,　the　numerical　experiment　is　also　included.