Visitor clustering lets you leverage customer characteristics to dynamically categorize visitors and generate cluster sets based on selected data inputs, thus identifying groups that have similar interests and behaviors for customer analysis and targeting.
The clustering process requires you to identify metrics and dimension elements to use as inputs, and allows you to choose a specific target population to apply these elements to create specified clusters. When you run the clustering process, the system uses the metric and dimension inputs to determine appropriate initial centers for the specified number of clusters. These centers are then used as a starting point to apply the K-Means algorithm.
|The initial centers are intelligently chosen via a Canopy Clustering pass.||Data clusters are created by associating every data point to the nearest center.||The mean of each of the K clusters becomes the new center.||The algorithm is repeated in steps 2 and 3 until convergence is reached. This can take multiples passes.|
The Maximum Iterations in the Options menu allows the analyst to specify the maximum number of iterations to be performed by the clustering algorithm. Setting this option may result in faster completion of the clustering process based on the maximum iterations cap at the expense of exact convergence of the cluster centers.
In the Cluster Builder, you can now select Options > Algorithm to select algorithms when defining clusters.
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.