Cluster within percentage of data

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Saurav Agarwal
Saurav Agarwal on 30 Jul 2013
Hi, I intend to form clusters from a data of 4 variables. These 4 variables are design parameters and each row of the data (200,000 X 4 matrix) correspond to a particular design. I wanted the clusters to be formed in such a way that the similar designs are clubbed together and we can deal with the centroid of the clusters instead of dealing with all the data sets. However, the kmeans cluster use euclidean distance to cluster. This does not serve the purpose as it would put (1,1000) and (1000,1) into the same cluster, in case of 2 variable format. The two designs would be completely different.
What I wanted was that the cluster contains the data sets which are x% of the variable values at the centroid of each cluster. Let's say we have a cluster with centroid (20,10,100,50), then all the data sets in the cluster should be (20+-2,10+-1,100+-10,50+-5) for x=10%. I couldn't find any method in the cluster analysis which could serve the above purpose. Please let me know if the logic I am trying to follow is flawed
  5 Comments
Saurav Agarwal
Saurav Agarwal on 2 Aug 2013
@Image I formed 60 clusters when using kmeans. However, I am not happy with the Euclidean method. I am rather trying to implement the relative distance. However, I am not able to progress.
Jing
Jing on 28 Aug 2013
So you know the centroid of each cluster or not? If you know the centroid, I'd say KNNSEARCH could help better. Anyway, your idea looks like a new algorithm to me...

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