Clustering related areas with k-means I am trying to cluster related areas of knowledge based in publications.For example a researcher has 3 keywords in a paper, in another paper he has 5 keywords, but 3 keywords are the same in the both papers. Then these 3 keywords are similar and It could be an area of knowledge. A sample of my file is:

'Domain Ontology,Semantic Web'
'Linked Data,Domain Ontology,Use Case'
'Domain Ontology,Linked Data,Semantic Annotation'
'GIS,Open GIS,Integrated Geo Systems'
'Open GIS,GIS'


My file contains 48963 rows and 19000 keywords. I have tried grouping words (like in the sample), using StringToWordVector (STWV), but I don't have good results. So I tried different K for my cluster since 3 until 13000. When K is greater my log likelihood decrease. In many models my words are grouped perfectly, but the percentages are not really good for some clusters. For example for K=3000, the 2999 have between 0,1% and 1% of the data, but the 3000 have the 21%. I am using WEKA. Anyone know some work related, some advice or any help is helpful...

Cheers.
I don't think k-means is the correct approach to this problem. You should look at doing topic modelling using latent semantic indexing.