Weaknesses of K-means clustering a Applicable only when the mean of objects is defined Need to specify k, the number of clusters in advance a Unable to handle noisy data and outliers a Not suitable to discover clusters with non-convex shapes, or clusters of very different sizeWeaknesses of K-means clustering ◼ Applicable only when the mean of objects is defined ◼ Need to specify k, the number of clusters, in advance ◼ Unable to handle noisy data and outliers ◼ Not suitable to discover clusters with non-convex shapes, or clusters of very different size