Interpreting cluster results
Hi all,
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?
Ben
Best Answer
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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:
https://www.dataiku.com/product/plugins/interactive-decision-tree-builder/
Answers
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Hi Ben,
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. -
ben_p Neuron 2020, Registered, Neuron 2021, Neuron 2022, Dataiku Frontrunner Awards 2021 Participant Posts: 143 ✭✭✭✭✭✭✭Options
Thanks Matt,
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?
Ben
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ben_p Neuron 2020, Registered, Neuron 2021, Neuron 2022, Dataiku Frontrunner Awards 2021 Participant Posts: 143 ✭✭✭✭✭✭✭Options
Thanks again Matt, when you say "modify the clustering", this has to be done manually, right?
Apologies if this is a dumb question!
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Regarding using interactive clustering. After reviewing several studies similar to my project I did this using the number of rows as the pre-cluster number and used the outlier detection as it's own cluster. I have 6 clusters and a 7th that is outliers, however, it is very hard to understand what is going on in the summary page or the heat map. Do you have a dumb downed version for me?
