Types of Clusters: Objective Function

$$ Clusters Defined by an Objective Function$$ Finds clusters that minimize or maximize an objective function. $$ Enumerate all possible ways of dividing the points into clusters and

evaluate the $`$goodness$\text{'}$ of each potential set of clusters by using

the given objective function. $($NP Hard$)$ Can have global or local objectives.

$$ Hierarchical clustering algorithms typically have local objectives

$$Partitional algorithms typically have global objectives$$ A variation of the global objective function approach is to fit the

data to a parameterized model.

$$ Parameters for the model are determined from the data.

$$ Mixture models assume that the data is a $$mixture$\text{'}$ of a number of

statistical distributions.