TITLE: Clustering algorithm to cluster objects based on their relation weight
QUESTION [6 upvotes]: I have $n$ words and their relatedness weight that gives me an $n\times n$ matrix. I'm going to use this for a search algorithm but the problem is I need to cluster the entered keywords based on their pairwise relation. So let's say if the keywords are {tennis,federer,wimbledon,london,police} and we have the following data from our weight matrix:
            tennis  federer  wimbledon  london  police      
tennis        1       0.8       0.6       0.4     0.0
federer       0.8      1        0.65      0.4     0.02
wimbledon     0.6     0.65       1        0.2     0.09
london        0.4     0.4       0.2        1      0.71
police        0.0     0.02      0.09      0.71     1

I need an algorithm to to cluster them into 2 clusters : {tennis,federer,wimbledon} {london,police}. Is there any known clustering algorithm than can deal with such thing ? I did some research, it appears that K-means algorithm is the most well known algorithm being used for clustering but apparently K-means doesn't suit this case.
I would greatly appreciate any help.

REPLY [2 votes]: You should feed a different matrix into k-means clustering, namely: 1-weights. Clustering algorithms require similar words to correspond to close points in space, and your weights are the opposite.