"Weight, Clustering and Decision Tree"

Question

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.

mariozupan · Answer

Ok. I think I understand. I need to study different clustering methods and measure performance. Rescaling and normalizing attributes at the same time to get meaningful clusters that satisfied my needs. Do you use some specific operator for rescaling? I have one more subquestion about clustering. I see the Statistica video tutorial about Kohonen SOM clustering. I tried SOM operator inside Rapidminer but I didn't got clusters, only dimensions, so I put k-means operator after SOM. Like this: Now k-means work with new dimensions which are derived from my 6 attributes. Is that make sense? Statistica and Matlab SOM toolbox tutorials shows that it is easy to interpret connection between attributes and SOM clusters. I can't find the way how to manage the same with Rapidminer. Look what I want to get: http://www.google.hr/url?sa=t&;rct=j&q=using%20self%20organizing%20maps%20to%20cluster%20stocks%20and%20financial%20ratios&source=web&cd=2&ved=0CCwQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.124.3253%26rep%3Drep1%26type%3Dpdf&ei=uWKCULTIK5DMsgbxioHIDg&usg=AFQjCNFY_aKuPeGVf7y2vGP2YJqja7KaSw

wessel · Answer

I don't fully understand your comments.

There is no easy solution to solve the rescaling or normalization issue.
You should understand how both the k-nearest neighbors and k-means clustering algorithms work.

Attributes with large scaling tend to get more weight in the distance calculation because the maximum possible distance as computed on these attributes is bigger. Similarly, nominal attributes are also weighted disproportionally, because a single nominal attribute counts as at least two numerical attributes after dummy coding (i.e. converting to binary attributes).

Why don't you try different lazy learners and see which one performs best?
If K* gives far superior performance to Euclidean distance then you know you should be worried.

Best regards,

Wessel

mariozupan · Answer

You mean normalize by size (i.e from -1 to +1) when you wrote "rescale"? I already normalize all attributes from -1 to +1. All tutorials and scientific papers about clustering and intepretation of clusters didn't mentioned procedures for clusters adjustments and logical explanation of clusters. If 20 companies are in the same cluster, it means that they are pretty close to the center of the cluster, according to a five attributes for example. Every row is the five-dimensional vector, right?
But how to correlate vector which is result of attributes normalization with the attributes?

Above questions applies to k-means, I will try network neighbour operator. I need to study network neighbour functioning, but you mean that in the case of financial indicator attribute network neigbour will be more suited then the k-meand and self-organized maps?