Purpose　-　It　is　well　known　that　the　selection　of　initial　cluster　centers　can　significantly　affect　K-means　clustering　results.　The　purpose　of　this　paper　is　to　propose　an　improved,　efficient　methodology　to　handle　such　a　challenge.　Design/methodology/approach　-　According　to　the　fact　that　the　inner-class　distance　among　samples　within　the　same　cluster　is　supposed　to　be　smaller　than　the　inter-class　distance　among　clusters,　the　algorithm　will　dynamically　adjust　initial　cluster　centers　that　are　randomly　selected.　Consequently,　such　adjusted　initial　cluster　centers　will　be　highly　representative　in　the　sense　that　they　are　distributed　among　as　many　samples　as　possible.　As　a　result,　local　optima　that　are　common　in　K-means　clustering　can　then　be　effectively　reduced.　In　addition,　the　algorithm　is　able　to　obtain　all　initial　cluster　centers　simultaneously　(instead　of　one　center　at　a　time)　during　the　dynamic　adjustment.　Findings　-　Experimental　results　demonstrate　that　the　proposed　algorithm　greatly　improves　the　accuracy　of　traditional　K-means　clustering　results　and,　in　a　more　efficient　manner.　Originality/value　-　The　authors　presented　in　this　paper　an　efficient　algorithm,　which　is　able　to　dynamically　adjust　initial　cluster　centers　that　are　randomly　selected.　The　adjusted　centers　are　highly　representative,　i.　e.　they　are　distributed　among　as　many　samples　as　possible.　As　a　result,　local　optima　that　are　common　in　K-means　clustering　can　be　effectively　reduced　so　that　the　authors　can　achieve　an　improved　clustering　accuracy.　In　addition,　the　algorithm　is　a　cost-efficient　one　and　the　enhanced　clustering　accuracy　can　be　obtained　in　a　more　efficient　manner　compared　with　traditional　K-means　algorithm.