Hello, students.

Today, I'm going to use clusterEnG to demonstrate the usage of

two publishing based algorithms, K- means and K-medoids.

As we have learned from the class,

K-Means is a very popular publishing based clustering algorithm.

To partition the given data points into k clusters.

K-means first selects k clusters centers randomly.

Then, for

every data point K-means computes distances to the current k centers.

Under sizes to the closest center.

After that, K-means needs to update the K centers by computing the mean of

each cluster, such a process is repeated until the K-centers do not change.

The K-matter is algorithm is very similar to the K-means algorithm.

With the only difference that we are not updating the center of each cluster we do

not compute the mean of all the current data points instead, we choose

one data point from the cluster that minimize the with-in cluster variance.

On clustering remember that there are three K steps.

Data preparation, Algorithm choosing and Result visualization.