Unsupervised Learning

we are just given output data, without any inputs. The goal is to discover interesting structure in the data

Discovering clusters

Let $K$ denote the number of clusters. Our first goal is to estimate the distribution over the number of clusters, $p(K|D)$

$K' = argmax_K\space p(K|D)$

Estimating which cluster each point belongs to

Let $z_i \in \{1, . . . , K\}$ represent the cluster to which data point i is assigned.

Real world applications

1. Autoclass system in astronomy
2. Clustering users into groups in e-commerce