Clustering algorithms such as k-means can be used to group similar data points together. A threshold can be defined and any point beyond that threshold can be classified as an anomaly. The problem with this approach is that the grouping created by k-means clustering may itself be biased because of the presence of anomalous data points and may affect the usefulness and accuracy of the approach.