  Cluster analysis is a field of data analysis that extracts underlying
patterns in data. One application of cluster analysis is in text-mining, the
analysis of large collections of text to find similarities between documents.
We used a collection of about 30,000 tweets extracted from Twitter just before
the World Cup started. A common problem with real world text data is the
presence of linguistic noise. In our case it would be extraneous tweets that
are unrelated to dominant themes. To combat this problem, we created an
algorithm that combined the DBSCAN algorithm and a consensus matrix. This way
we are left with the tweets that are related to those dominant themes. We then
used cluster analysis to find those topics that the tweets describe. We
clustered the tweets using k-means, a commonly used clustering algorithm, and
Non-Negative Matrix Factorization (NMF) and compared the results. The two
algorithms gave similar results, but NMF proved to be faster and provided more
easily interpreted results. We explored our results using two visualization
tools, Gephi and Wordle.
