Clustering 2.0

The Cluster Builder now includes a KMeans++ algorithm (only the KMeans algorithm was previously supported) that uses a faster approach to finding centers for an expedited cluster-generation process.

KMeans Algorithms

In the Cluster Builder, you can now select Options > Algorithm to select algorithms when defining clusters.

• KMeans. This algorithm uses canopy clustering to define the centers of the cluster.
• KMeans++. This algorithm expedites cluster building when running against large sets of data.

KMeans++ is an improved implementation of KMeans clustering algorithm because it provides better initialization of initial k centers. (The original KMeans algorithm chooses initial centers randomly.) KMeans++ selects the first center randomly. The remaining k-1 centers will be chosen one by one based on the distance a data point is to the closest existing center. The furthest data points have a better chance to be chosen as a new center than nearby data points. After the initial center is chosen, the procedure is performed exactly the same as the original KMeans clustering.

The workflow for KMeans++ is exactly the same as the workflow for KMeans clustering, except that you need to select Options > Algorithm > KMeans++ in the cluster builder.