### Examining predicted Clusters and Cluster details using SQL

In a previous blog post I gave some details of how you can examine some of the details behind a prediction made using a classification model. This seemed to spark a lot of interest. But before I come back to looking at classification prediction details and other information, this blog post is the first in a 4 part blog post on examining the details of Clusters, as identified by a cluster model created using Oracle Data Mining.

The 4 blog posts will consist of:

- 1 – (this blog post) will look at how to determine the predicted cluster and cluster probability for your record.
- 2 – will show you how to examine the details behind and used to predict the cluster.
- 3 – A record could belong to many clusters. In this blog post we will look at how you can determine what clusters a record can belong to.
- 4 – Cluster distance is a measure of how far the record is from the cluster centroid. As a data point or record can belong to many clusters, it can be useful to know the distances as you can build logic to perform different actions based on the cluster distances and cluster probabilities.

Right. Let’s have a look at the first set of these closer functions. These are CLUSTER_ID and CLUSTER_PROBABILITY.

CLUSER_ID : Returns the number of the cluster that the record most closely belongs to. This is measured by the cluster distance to the centroid of the cluster. A data point or record can belong or be part of many clusters. So the CLUSTER_ID is the cluster number that the data point or record most closely belongs too.

CLUSTER_PROBABILITY : Is a probability measure of the likelihood of the data point or record belongs to a cluster. The cluster with the highest probability score is the cluster that is returned by the CLUSTER_ID function.

Now let us have a quick look at the SQL for these two functions. This first query returns the cluster number that each record most strong belongs too.

SELECT customer_id, cluster_id(clus_km_1_37 USING *) as Cluster_Id, FROM insur_cust_ltv_sample WHERE customer_id in ('CU13386', 'CU6607', 'CU100');

Now let us add in the cluster probability function.

SELECT customer_id, cluster_id(clus_km_1_37 USING *) as Cluster_Id, cluster_probability(clus_km_1_37 USING *) as cluster_Prob FROM insur_cust_ltv_sample WHERE customer_id in ('CU13386', 'CU6607', 'CU100');

These functions gives us some insights into what the cluster predictive model is doing. In the remaining blog posts in this series I will look at how you can delve deeper into the predictions that the cluster algorithm is make.