Hierarchical clustering - Finds nested groups of cluster.
K-means - Quick way of finding clusters in quantitative data
Distances
Euclidean Distance
Euclidean distance only makes sense when all the data is real-valued (quantitative)
Hamming Distance
For categorical variables (e.g., male/female, or small/medium/large), user can define the distance as 0 if two points are in the same category and 1 otherwise, which counts the number of mismatches
Manhattan Distance
Manhattan distance measures distance in the number of horizontal and vertical units it takes to get from one (real-valued) point to the other