the following are some disadvantages of -means clustering algorithmsit is bit difficult to predict the number of clusters the value of output is strongly impacted by initial inputs like number of clusters (value of korder of data will have strong impact on the final output it is very sensitive to rescaling if we will rescale our data by means of normalization or standardizationthen the output will completely change it is not good in doing clustering job if the clusters have complicated geometric shape applications of -means clustering algorithm the main goals of cluster analysis areto get meaningful intuition from the data we are working with cluster-then-predict where different models will be built for different subgroups to fulfill the above-mentioned goalsk-means clustering is performing well enough it can be used in following applicationsmarket segmentation document clustering image segmentation image compression customer segmentation analyzing the trend on dynamic data