Being that clustering is a distance-based algorithm, outliers can have multiple undesired effects on the quality of the clusters produced. Being the objective of K-Means is to minimize the within cluster sum of squares, or distance from each observation to the clusterâ€™s centroid, outliers that are far from the centroids will prevent the objective from achieving a minimum compared to if they were not present. It is also possible that the presence of a small number of outliers can result in clusters that only contain a few observations, which can obscure the practical conclusions of what the clusters represent. This further emphasizes the importance of scaling the data before a clustering algorithm is trained, but even after scaling, noticeable outliers should be investigated further.