In this chapter, we discussed various methods of segmentation for unsupervised data mining problems and their implementations. Clustering is a subjective segmentation method and it requires further investigation such as profiling by cluster and calculating different measures such as mean and median for different variables. If any cluster displays closeness to another cluster and from business point of view they make sense, then they can be combined together. We discussed which model to use where and what the data requirement for each of the models is. In this chapter, we specifically highlighted the importance of data visualization when it comes to representing clustering solutions.