I am testing our clustering on a data table of customer records. As a starting point I tried Interactive Clustering on a dataset of 6.5 million records, with approx. 10 columns containing stats on a records behaviour.
I am slightly puzzled by the results, shown below:
I have almost all my customers in a single cluster, with only a handful falling into other clusters - why would clustering yield such results and how might I go about making the clusters more useful?
In interactive clustering, we first run a K-mean algorithm.
K-mean is sensitive to outliers and noise. So in your case, you end with all the observations in the same cluster and 4 clusters of outliers.
To have better results you can try to use in Outliers Detection in the Design part: Create a cluster with outliers.
You'll have only one cluster with outliers.
I ran a simpler k-means on the data and got much more balanced segments - I still don't understand how this two-step clustering provides extra insight into the clusters and allows them to be explored after clustering, can you explain this?
Interactive clustering is a 2 steps process.
First you train a K-mean, then you can modify yourself the clustering, merging 2 clusters together for example.
If you are interested to do your own grouping of data, you can check also the interactive decision tree builder: