Scalable Techniques for Clustering the Web

By Taher H. Haveliwala, Aristides Gionis, Piotr Indyk. 2000

189 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

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Citations in the corpus

Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “Popular techniques for c-NN are often based on Locality Sensitive Hashing (LSH) [12], which is a family of functions with the nice property that more similar objects in the domain of these functions have a higher probability of colliding in the range space than less similar ones.”
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality (2012, methodology): “They have been successfully applied to computational problems in a variety of areas, including web clustering [HGI00], computational biology [Buh01, BT01], computer vision (see selected articles in [SDI06]), computational drug design [DGJC06] and computational linguistics [RPH05].”
Detecting near-duplicates for web crawling (2007, methodology): “Several experimental studies have tested the e.cacy of min-hash in various settings (Cohen et al [21] for associationrule mining, Chen et al [18] for selectivity estimation of boolean queries, Gionis et al [30] for indexing set-value predicates and Haveliwala [33] for web-clustering). c) Signatu”
Efficient exact set-similarity joins (2006, methodology): “As mentioned earlier, several algorithms have been proposed for SSJoin in previous work [6, 8, 15, 19, 22].”
Locality-sensitive hashing scheme based on p-stable distributions (2004, background): “This is due to the fact that, in many cases, approximate nearest neighbor is almost as good as the exact one; in particular, if the distance measure accurately captures the notion of user quality, then small differences in the distance should not matter.”
Scalable clustering of news search results (2011, methodology): “Our offline clustering system draws from the work of [18].”
Similarity estimation techniques from rounding algorithms (2002, methodology): “This was exploited later in several experimental papers: Cohen et al [14] for association-rule mining, Haveliwala et al [27] for clustering web documents, Chen et al [13] for selectivity estimation of boolean queries, Chen et al [12] for twig queries, and Gionis et al [22] for indexing set value”
A Sketch Algorithm for Estimating Two-Way and Multi-Way Associations (2007, methodology): “…rules (Brin et al. 1997; Brin, Motwani, and Silverstein 1997), clustering (Guha, Rastogi, and Shim 1998; Broder 1998; Aggarwal et al. 1999; Haveliwala, Gionis, and Indyk 2000; Haveliwala et al. 2002), query optimization (Matias, Vitter, and Wang 1998; Chaudhuri, Motwani, and Narasayya…”
Scalable, Behavior-Based Malware Clustering (2009, methodology): “In [27], LSH is used to generate the set of approximate near pairs |S|, but there are no similarity computations.”
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions (2006, methodology): “The LSH algorithm and its variants has been successfully applied to computational problems in a variety of areas, including web clustering [23], computational biology [10.11], computer vision (see selected articles in [ 23 ]), computational drug design [18] and computational linguistics [34].”
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS) (2014, methodology): “In case of L2-ALSH, due to asymmetry, we loose the capability to generate LSH like random data partitions for efficient clustering [12].”