"Weight, Clustering and Decision Tree"
I have 300 companies that I want to divide in clusters, according to a financial performance indicators. Then I want to describe every cluster with Decision Tree.
So, I have a few questions:
1. My attributes (financial indicators) are not normally distributed. I tried some statistical tests. Is it matter?
2. My attributes have different ranges. Do I need normalization operator?
3. Do I need some selecting by weight operator for choosing indicators which are significant or k-means make clusters according to a attributes weight?
As you can see from above questions that I tried something but I didn't get clusters that I can describe as "good" "better" "the best". I need an answer as soon as it is possible. Small example, or even data miner who is willing to create cluster on my data for a decent fee.
So, I have a few questions:
1. My attributes (financial indicators) are not normally distributed. I tried some statistical tests. Is it matter?
2. My attributes have different ranges. Do I need normalization operator?
3. Do I need some selecting by weight operator for choosing indicators which are significant or k-means make clusters according to a attributes weight?
As you can see from above questions that I tried something but I didn't get clusters that I can describe as "good" "better" "the best". I need an answer as soon as it is possible. Small example, or even data miner who is willing to create cluster on my data for a decent fee.