Fast approximate text document clustering using compressive sampling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6912 LNAI, Issue PART 2, 2011, Pages 565-580

  Park, Laurence A F ^a  

^a UNIVERSITY OF WESTERN SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSIVE SAMPLING; COMPUTATIONAL CONSTRAINTS; DOCUMENT CLUSTERING; EMPIRICAL RESULTS; K-MEANS CLUSTERING; NUMBER OF SAMPLES; SAMPLING TECHNIQUE; TEXT DOCUMENT;

CLUSTER ANALYSIS; DISCRETE COSINE TRANSFORMS; DISCRETE FOURIER TRANSFORMS; LEARNING SYSTEMS; TEXT PROCESSING;

INFORMATION RETRIEVAL;

EID: 80052411943     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23783-6_36     Document Type: Conference Paper

References (9)

1. Baraniuk, R., Davenport, M., DeVore, R., Wakin, M.: A simple proof of the restricted isometry property for random matrices. Constructive Approximation 28, 253-263 (2008), doi:10.1007/s00365-007-9003-x
2. Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 245-250. ACM, New York (2001)
3. Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Transactions on Information Theory 51(12), 4203-4215 (2005)
4. Candes, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21-30 (2008)
5. Candes, E.J.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians. European Mathematical Society, Madrid (2006)
6. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on Very Large Data Bases, VLDB 1999, pp. 518-529. Morgan Kaufmann Publishers Inc., San Francisco (1999)
7. Goyal, V.K., Fletcher, A.K., Rangan, S.: Compressive sampling and lossy compression. IEEE Signal Processing Magazine 25(2), 48-56 (2008)
8. Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1), 100-108 (1979)
9. Li, P., Church, K.W., Hastie, T.J.: Conditional random sampling: A sketch-based sampling technique for sparse data. In: Advances in Neural Information Processing Systems, vol. 19, p. 873 (2007)