Table 6: D: Modified K-tree, Random Indexing, BM25 + LF-IDF Table 7: E: Modified K-tree, Random Indexing, BM25 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 7 Conclusion [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. Dimensions α β γ δ 100 3.1527 0.0227 0.3105 0.0047 200 3.0589 0.0266 0.3312 0.0065 400 2.9014 0.0259 0.3726 0.0065 800 2.6690 0.0336 0.4204 0.0085 1000 2.5890 0.0319 0.4349 0.0090 2000 2.3882 0.0428 0.4700 0.0129 4000 2.2558 0.0443 0.4879 0.0144 8000 2.1933 0.0473 0.4935 0.0162 10000 2.1735 0.0496 0.4969 0.0171 Dimensions α β γ δ 100 3.0717 0.0263 0.3269 0.0074 200 2.9078 0.0291 0.3706 0.0087 400 2.6832 0.0293 0.4191 0.0077 800 2.4696 0.0350 0.4555 0.0106 1000 2.4093 0.0399 0.4673 0.0115 2000 2.2826 0.0422 0.4853 0.0137 4000 2.2094 0.0416 0.4937 0.0141 8000 2.1764 0.0429 0.4975 0.0149 10000 2.1686 0.0440 0.4981 0.0161