"K-Means Clustering with Mixed Attributes"

khannadh

Hello Everyone,
I want to segment my customer base (13,000 customers) according to several attributes such as:

1. Total Deposits (numerical)
2. Total #Accounts (integer)
3. #Months Since Customer Acquisition (Integer)
4. Has the client subscribed for Online Banking or not? (Categorical)

I want to see what is common among my customers by splitting them into clusters.

I have mixed attributes in my data set (numerical and categorical).

The questions I have are:
1. What is the best distance measure in this case?
2. Do I need to transform any attribute?
3. Do I need to normalize any attribute?
4. What is the best way to set up the model?

Any help would be appreciated.

Thank You

MartinLiebig

Hi!

1. What is the best distance measure in this case?

There is no "best". You simply need to try it. I would recommend trying Euclidian, Manhattan and Cosine Similiarity

2. Do I need to transform any attribute?

Probably yes. I would use Nominal to Numerical to make the Categorial value to 0/1 variables. Otherwise you can only use MixedEuclidianDistance.

3. Do I need to normalize any attribute?

Almost for sure. Otherwise you would introduce a (most likely not wanted) implicit weightening of your attributes

4. What is the best way to set up the model?

I'm not sure what you mean by this.

A general tip: You can analyse your clusters by taking it as label and user Feature Selection techniques (e.g. Weight by Gini Index or a Forward Selection). Then you get the most important attributes distingiushing the clusters.
If you use a one vs all strategy you can answer the question "What distinguishes cluster1 from the others?" which might be really helpful for interpreting results.

Second tip: If you use any other distance than euclidian distance, you should not use k-Means but k-Menoids. Otherwise the algorithm might not converge.

Cheers,

Martin