## 9.4 Summary

In this chapter we have focused on just two types of clustering methods: partitioning methods (K-means and model-based methods); and hierarchical agglomerative clustering methods.

There are many algorithms for performing Cluster Analysis, but there is no generally accepted *best* method. Different algorithms (or even the same algorithm with a different initialisation) do not necessarily produce the same results on a given dataset, and there is often a fairly large subjective element in the assessment of any particular method. Moreover, the choice of which underlying distance function to use can also lead to different conclusions.

One way to test a clustering algorithm is to apply it on data with a known group structure. Experience suggests that this will only produce good results when the groups are very distinct. When, on the other hand, there is a lot of overlap between groups, clustering algorithms are not likely to perform particularly well.

However, despite these cautionary remarks, clustering algorithms are often useful in practice, but it is an area where usually the most one can hope for is to find a good, but sub-optimal, solution.