# Clustering

### Cluster Distance using SQL with Oracle Data Mining – Part 4

This is the fourth and last blog post in a series that looks at how you can examine the details of predicted clusters using Oracle Data Mining. In the previous blog posts I looked at how to use CLUSER_ID, CLUSTER_PROBABILITY and CLUSTER_SET.

In this blog post we will look at CLUSTER_DISTANCE. We can use the function to determine how close a record is to the centroid of the cluster. Perhaps we can use this to determine what customers etc we might want to focus on most. The customers who are closest to the centroid are one we want to focus on first. So we can use it as a way to prioritise our workflows, particularly when it is used in combination with the value for CLUSTER_PROBABILITY.

Here is an example of using CLUSTER_DISTANCE to list all the records that belong to Cluster 14 and the results are ordered based on closeness to the centroid of this cluster.

SELECT customer_id, cluster_probability(clus_km_1_37 USING *) as cluster_Prob, cluster_distance(clus_km_1_37 USING *) as cluster_Distance FROM insur_cust_ltv_sample WHERE cluster_id(clus_km_1_37 USING *) = 14 order by cluster_Distance asc;

Here is a subset of the results from this query.

When you examine the results you may notice that the records that is listed first and closest record to the centre of cluster 14 has a very low probability. You need to remember that we are working in a N-dimensional space here. Although this first record is closest to the centre of cluster 14 it has a really low probability and if we examine this record in more detail we will find that it is at an overlapping point between a number of clusters.

This is why we need to use the CLUSTER_DISTANCE and CLUSTER_PROBABILITY functions together in our workflows and applications to determine how we need to process records like these.

### Cluster Sets using SQL with Oracle Data Mining – Part 3

This is the third blog post on my series on examining the Clusters that were predicted by an Oracle Data Mining model. Check out the previous blog posts.

- Part 1 – Examining predicted Clusters and Cluster details using SQL
- Part 2 – Cluster Details with Oracle Data Mining

In the previous posts we were able to list the predicted cluster for each record in our data set. This is the cluster that the records belonged to the most. I also mentioned that a record could belong to many clusters.

So how can you list all the clusters that the a record belongs to?

You can use the CLUSTER_SET SQL function. This will list the Cluster Id and a probability measure for each cluster. This function returns a array consisting of the set of all clusters that the record belongs to.

The following example illustrates how to use the CLUSTER_SET function for a particular cluster model.

SELECT t.customer_id, s.cluster_id, s.probability FROM (select customer_id, cluster_set(clus_km_1_37 USING *) as Cluster_Set from insur_cust_ltv_sample WHERE customer_id in ('CU13386', 'CU100')) T, TABLE(T.cluster_set) S order by t.customer_id, s.probability desc;

The output from this query will be an ordered data set based on the customer id and then the clusters listed in descending order of probability. The cluster with the highest probability is what would be returned by the CLUSTER_ID function. The output from the above query is shown below.

If you would like to see the details of each of the clusters and to examine the differences between these clusters then you will need to use the CLUSTER_DETAILS function (see previous blog post).

You can specify topN and cutoff to limit the number of clusters returned by the function. By default, both topN and cutoff are null and all clusters are returned.

– topN is the N most probable clusters. If multiple clusters share the Nth probability, then the function chooses one of them.

– cutoff is a probability threshold. Only clusters with probability greater than or equal to cutoff are returned. To filter by cutoff only, specify NULL for topN.

You may want to use these individually or combined together if you have a large number of customers. To return up to the N most probable clusters that are greater than or equal to cutoff, specify both topN and cutoff.

The following example illustrates using the topN value to return the top 4 clusters.

SELECT t.customer_id, s.cluster_id, s.probability FROM (select customer_id, cluster_set(clus_km_1_37, 4, nullUSING *) as Cluster_Set from insur_cust_ltv_sample WHERE customer_id in ('CU13386', 'CU100')) T, TABLE(T.cluster_set) S order by t.customer_id, s.probability desc;

and the output from this query shows only 4 clusters displayed for each record.

Alternatively you can select the clusters based on a cut off value for the probability. In the following example this is set to 0.05.

SELECT t.customer_id, s.cluster_id, s.probability FROM (select customer_id, cluster_set(clus_km_1_37, NULL, 0.05USING *) as Cluster_Set from insur_cust_ltv_sample WHERE customer_id in ('CU13386', 'CU100')) T, TABLE(T.cluster_set) S order by t.customer_id, s.probability desc;

and the output this time looks a bit different.

Finally, yes you can combine these two parameters to work together.

SELECT t.customer_id, s.cluster_id, s.probability

FROM (select customer_id, cluster_set(clus_km_1_37**, 2, 0.05** USING *) as Cluster_Set

from insur_cust_ltv_sample

WHERE customer_id in (‘CU13386’, ‘CU100’)) T,

TABLE(T.cluster_set) S

order by t.customer_id, s.probability desc;

### 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.