  Text clustering is arguably one of the most important topics in modern data
mining. Nevertheless, text data require tokenization which usually yields a
very large and highly sparse term-document matrix, which is usually difficult
to process using conventional machine learning algorithms. Methods such as
Latent Semantic Analysis have helped mitigate this issue, but are nevertheless
not completely stable in practice. As a result, we propose a new feature
agglomeration method based on Nonnegative Matrix Factorization, which is
employed to separate the terms into groups, and then each group's term vectors
are agglomerated into a new feature vector. Together, these feature vectors
create a new feature space much more suitable for clustering. In addition, we
propose a new deterministic initialization for spherical K-Means, which proves
very useful for this specific type of data. In order to evaluate the proposed
method, we compare it to some of the latest research done in this field, as
well as some of the most practiced methods. In our experiments, we conclude
that the proposed method either significantly improves clustering performance,
or maintains the performance of other methods, while improving stability in
results.

Machine Learning;Machine Learning