content_image/413514.jpg content_image/413515.jpg
<img src='content_image/413514.jpg'>

<img src='content_image/413515.jpg'>

## 7 Conclusion 

RI K-tree was introduced as an attractive approach for large scale document clustering. This is the first time RI and K-tree have been combined. The results show that RI K-tree improves quality of clustering results, even over the unexpected results found when using TF- IDF culling. Further experiments are required to deter- mine if the unexpected effect of TF-IDF culling at low dimensions is an anomaly or actually exists in many collections. Additionally, RI K-tree is an efficient and high quality approach to overcome previous problems with sparse representations when using K-tree. Unfor- tunately the combination of BM25 and LF-IDF repre- sentations did not improve results in clustering as they did in earlier classification results. 

## References 

[1] K-tree project page, http://ktree.sourceforge.net. 2009. 

[2] D. Achlioptas. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences , Volume 66, Number 4, pages 671–687, 2003. 

[3] D. Arthur and S. Vassilvitskii. k-means++: the advan- tages of careful seeding. In SODA ’07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms , pages 1027–1035, Philadelphia, PA, USA, 2007. Society for Industrial and Applied Mathematics. 

[4] E. Bingham and H. Mannila. Random projection in dimensionality reduction: applications to image and text data. In KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining , pages 245–250, New York, NY, USA, 2001. ACM. 

[5] C.M. De Vries and S. Geva. Document clustering with k-tree. Advances in Focused Retrieval: 7th Interna- tional Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008, Dagstuhl Castle, Germany, December 15-18, 2008. Revised and Selected Papers , pages 420–431, 2009. 

[6] C.M. De Vries and S. Geva. K-tree: large scale document clustering. In SIGIR ’09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval , pages 718– 719, New York, NY, USA, 2009. ACM. 

[7] S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Lan- dauer and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Infor- mation Science , Volume 41, Number 6, pages 391–407, 1990. 

[8] L. Denoyer and P. Gallinari. The Wikipedia XML Corpus. SIGIR Forum , 2006. 

[9] A. Gersho and R.M. Gray. Vector quantization and sig- nal compression . Kluwer Academic Publishers, 1993. 

[10] S. Geva. K-tree: a height balanced tree structured vector quantizer. Proceedings of the 2000 IEEE Signal Pro- cessing Society Workshop Neural Networks for Signal Processing X, 2000. , Volume 1, pages 271–280 vol.1, 2000. 

[11] W.B. Johnson and J. Lindenstrauss. Extensions of Lipschitz mappings into a Hilbert space. Contemporary mathematics , Volume 26, Number 189-206, pages 1–1, 1984. 

[12] P. Kanerva. The spatter code for encoding concepts at many levels. In ICANN94, Proceedings of the International Conference on Artificial Neural Networks , 1994. 

[13] G. Karypis. CLUTO-A Clustering Toolkit. 2002. 

[14] S. Lloyd. Least squares quantization in pcm. In- formation Theory, IEEE Transactions on , Volume 28, Number 2, pages 129–137, March 1982. 

[15] T.A. Plate. Distributed representations and nested compositional structure . Ph.D. thesis, 1994. 

[16] M.F. Porter. An algorithm for suffix stripping. Pro- gram: Electronic Library and Information Systems , Volume 40, Number 3, pages 211–218, 2006. 

[17] S.E. Robertson and K.S. Jones. Simple, proven ap- proaches to text retrieval. Update , 1997. 

[18] M. Sahlgren. An introduction to random indexing. In Methods and Applications of Semantic Indexing Work- shop at the 7th International Conference on Terminol- ogy and Knowledge Engineering, TKE 2005 , 2005. 

[19] G.K. Zipf. Human behavior and the principle of least effort: An introduction to human ecology . Addison- Wesley Press, 1949. 

