@article{(Open Science Index):https://publications.waset.org/pdf/9998179,
	  title     = {Growing Self Organising Map Based Exploratory Analysis of Text Data},
	  author    = {Sumith Matharage and  Damminda Alahakoon},
	  country	= {},
	  institution	= {},
	  abstract     = {Textual data plays an important role in the modern
world. The possibilities of applying data mining techniques to
uncover hidden information present in large volumes of text
collections is immense. The Growing Self Organizing Map (GSOM)
is a highly successful member of the Self Organising Map family
and has been used as a clustering and visualisation tool across wide
range of disciplines to discover hidden patterns present in the data.
A comprehensive analysis of the GSOM’s capabilities as a text
clustering and visualisation tool has so far not been published. These
functionalities, namely map visualisation capabilities, automatic
cluster identification and hierarchical clustering capabilities are
presented in this paper and are further demonstrated with experiments
on a benchmark text corpus.
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {8},
	  number    = {4},
	  year      = {2014},
	  pages     = {639 - 646},
	  ee        = {https://publications.waset.org/pdf/9998179},
	  url   	= {https://publications.waset.org/vol/88},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 88, 2014},