Fast growing self organizing map for text clustering

This paper presents an integration of a novel document vector representation technique and a novel <span class="ScopusTermHighlight">Growing</span> <span class="ScopusTermHighlight">Self</span> <span class="ScopusTermHighlight">Organizing</span> Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document. <br /><br />An index based similarity calculation method is employed on this low dimensional feature space and the <span class="ScopusTermHighlight">growing</span> <span class="ScopusTermHighlight">self o</span><span class="ScopusTermHighlight">rganizing </span>process is modified to comply with the new feature representation model. <br /><br />The initial experiments show that this novel integration outperforms the state-of-the-art <span class="ScopusTermHighlight">Self </span><span class="ScopusTermHighlight">Organizing</span> <span class="ScopusTermHighlight">Map</span> based techniques of <span class="ScopusTermHighlight">text</span> <span class="ScopusTermHighlight">clustering</span> in terms of its efficiency while preserving the same accuracy level.