Many　clustering　algorithms　work　well　on　small　data　sets　of　less　than　200　data　objects.　However,　a　large　database　may　contain　millions　of　objects,　and　clustering　on　such　a　large　data　set　may　lead　to　biased　results.　As　data　volumes　and　availability　continue　to　grow,　so　does　the　need　for　large　dataset　analytics.　Among　the　most　commonly　used　clustering　algorithms,　K-means　proved　to　be　one　of　the　most　popular　choices　to　provide　acceptable　results　in　a　reasonable　amount　of　time.　In　this　paper,　we　present　an　improved　k-means　algorithm　with　better　initial　centroids.　Also,　we　implement　this　modified　algorithm　on　Hadoop　platform.　Experiments　show　that　the　improved　k-means　algorithm　converges　faster　than　the　classic　k-means　and　the　average　execution　time　is　reduced　compared　to　the　traditional　k-means.