K-Means-for-Yahoo-Click-Log

Dataset can be obtained from https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&did=49

The project is mainly to demonstrate the performance in terms of convergence for Random Initialisation and K++ for K-Means Algorithm.

K-Means is a clustering algorithm commonly used in unsupervised learning.

It aims at partitioning the dataset into K partitions.

Random Initialisation

• The cluster centroids are picked at random from the data instances.

K++

• Picks points that are as far away as possible

• This helps in picking points in a smarter way