  Document clustering is a traditional, efficient and yet quite effective, text
mining technique when we need to get a better insight of the documents of a
collection that could be grouped together. The K-Means algorithm and the
Hierarchical Agglomerative Clustering (HAC) algorithm are two of the most known
and commonly used clustering algorithms; the former due to its low time cost
and the latter due to its accuracy. However, even the use of K-Means in text
clustering over large-scale collections can lead to unacceptable time costs. In
this paper we first address some of the most valuable approaches for document
clustering over such 'big data' (large-scale) collections. We then present two
very promising alternatives: (a) a variation of an existing K-Means-based fast
clustering technique (known as BigKClustering - BKC) so that it can be applied
in document clustering, and (b) a hybrid clustering approach based on a
customized version of the Buckshot algorithm, which first applies a
hierarchical clustering procedure on a sample of the input dataset and then it
uses the results as the initial centers for a K-Means based assignment of the
rest of the documents, with very few iterations. We also give highly efficient
adaptations of the proposed techniques in the MapReduce model which are then
experimentally tested using Apache Hadoop and Spark over a real cluster
environment. As it comes out of the experiments, they both lead to acceptable
clustering quality as well as to significant time improvements (compared to
K-Means - especially the Buckshot-based algorithm), thus constituting very
promising alternatives for big document collections.
