|Name|Definition|Example|Characteristics|Organization|
|----:|:---------:|:-----:|:-------------:|:---------:|
|Connectivity Models| Data points closer in data space are more similar than those far away| hierachical cluster| easy to interpret but do not scale well|Hierachical|
|Centroid models|iterative where similarity is intepreted as proximity of data point to centroid| K-means|provide final number of cluster| Non-Hierachical|
|Distribution Models|Based on probability of data points in a cluster belonging to the same distribution | EM-Algorithm (Expectation-Maximization) | frequent problems of overfitting|Non-Hierachical|
|Density Models| Isolate different density regions as basis for clustering| Density-Based Clustering of Application with Noise (DBSCAN)| Not good on high dimensional data or clusters with varying densities|Non-Hierachical|