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CHAPTER 3. FINDING SIMILAR ITEMS
5. S. Chaudhuri, V. Ganti, and R. Kaushik, “A primitive operator for sim-
ilarity joins in data cleaning,” Proc. Intl. Conf. on Data Engineering,
2006.
6. M. Datar, N. Immorlica, P. Indyk, and V.S. Mirrokni, “Locality-sensitive
hashing scheme based on p-stable distributions,” Symposium on Compu-
tational Geometry pp. 253–262, 2004.
7. A. Gionis, P. Indyk, and R. Motwani, “Similarity search in high dimen-
sions via hashing,” Proc. Intl. Conf. on Very Large Databases, pp. 518–
529, 1999.
8. M. Henzinger, “Finding near-duplicate web pages: a large-scale evaluation
of algorithms,” Proc. 29th SIGIR Conf., pp. 284–291, 2006.
9. P. Indyk and R. Motwani. “Approximate nearest neighbor: towards re-
moving the curse of dimensionality,” ACM Symposium on Theory of Com-
puting, pp. 604–613, 1998.
10. P. Li, A.B. Owen, and C.H. Zhang. “One permutation hashing,” Conf.
on Neural Information Processing Systems 2012, pp. 3122–3130.
11. U. Manber, “Finding similar ﬁles in a large ﬁle system,” Proc. USENIX
Conference, pp. 1–10, 1994.
12. M. Theobald, J. Siddharth, and A. Paepcke, “SpotSigs: robust and eﬃ-
cient near duplicate detection in large web collections,” 31st Annual ACM
SIGIR Conference, July, 2008, Singapore.
13. C. Xiao, W. Wang, X. Lin, and J.X. Yu, “Eﬃcient similarity joins for
near duplicate detection,” Proc. WWW Conference, pp. 131-140, 2008.
