I have a data of customers and i want to split them to segment (profiling). The columns of the data are Amount-Spending, Amount-Bonus, Age, Churn-or-Not. So i clustered my data with k-means. to 3 clusters. Now, how can i show that the first column of the data ('Amount-Spending') is more close to cluster number 1 for example. So i can say that cluster number 1 are people that usually spend a lot of money and not just, customer number 5 is in cluster 1.
It seems to me that you are in danger to misuse Cluster Analysis, which is a technique to discover structure in data and not to describe a structure that one just assumes to exist. To see what I mean, ask yourself the following questions for example:
- Why are you clustering into 3 groups? Not 4 or 9?
- When plotting the data (luckily there are 3 attributes only), do you see any groups? How many? How can these groups roughly be described using the values of the attributes?
- KMeans uses the Euklidean distance as a measure of similarity of samples. Is this the appropriate measure in your case?