Unsupervised classification methods available

Question

Hi all

I'm doing a research which involves "unsupervised classification". Basically I have a trainSet and I want to cluster data in X number of classes in unsupervised way. Idea is similar to what k-means does.

Let's say

Step1) featureSet is a [1057x10] matrice and I want to cluster them into 88 clusters.

Step2) Use previously calculated classes to compute how does the testData is classified

Question -Is it possible to do it with SVM or N-N ? Anything else ? -Any other recommendations ?

Answer 1

This sounds like a classic clustering problem. Neither SVMs or neural networks are going to be able to directly solve this problem. You can use either approach for dimensionality reduction, for example to embed your 10-dimensional data in two-dimensional space, but they will not put the data into clusters for you.

There are a huge number of clustering algorithms besides k-means. If you wanted a contrasting approach, you might want to try an agglomerative clustering algorithm. I don't know what kind of computing environment you are using, but I quite like R and this (very) short guide on clustering.

Answer 2

There are many clustering algorithms out there, and the web is awash with information on them and sample implementations. A good starting point is the Wikipedia entry on cluster analysis Cluster_analysis.

As you have a working k-means implementation, you could try one of the many variants to see if they yeild better results (k-means++ perhaps, seeing as you mentioned SVM). If you want a completely different approach, have a look at Kohonen Maps - also called Self Organising Feature Maps. If that looks too tricky, a simple hierarchical clustering would be easy to implement (find the nearest two items, combine, rinse and repeat).