  Nowadays, document clustering is considered as a data intensive task due to
the dramatic, fast increase in the number of available documents. Nevertheless,
the features that represent those documents are also too large. The most common
method for representing documents is the vector space model, which represents
document features as a bag of words and does not represent semantic relations
between words. In this paper we introduce a distributed implementation for the
bisecting k-means using MapReduce programming model. The aim behind our
proposed implementation is to solve the problem of clustering intensive data
documents. In addition, we propose integrating the WordNet ontology with
bisecting k-means in order to utilize the semantic relations between words to
enhance document clustering results. Our presented experimental results show
that using lexical categories for nouns only enhances internal evaluation
measures of document clustering; and decreases the documents features from
thousands to tens features. Our experiments were conducted using Amazon Elastic
MapReduce to deploy the Bisecting k-means algorithm.
