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Nearoptimal hashing algorithms for approximate nearest neighbor in high dimensions
, 2008
"... In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The ..."
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Cited by 457 (7 self)
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In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The problem is of significant interest in a wide variety of areas.
Data Clustering: 50 Years Beyond KMeans
, 2008
"... Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and m ..."
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Cited by 294 (7 self)
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Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is exploratory in nature to find structure in data. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, Kmeans, was first published in 1955. In spite of the fact that Kmeans was proposed over 50 years ago and thousands of clustering algorithms have been published since then, Kmeans is still widely used. This speaks to the difficulty of designing a general purpose clustering algorithm and the illposed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semisupervised clustering, ensemble clustering, simultaneous feature selection, and data clustering and large scale data clustering.
Finding motifs using random projections
, 2001
"... Pevzner and Sze [23] considered a precise version of the motif discovery problem and simultaneously issued an algorithmic challenge: find a motif Å of length 15, where each planted instance differs from Å in 4 positions. Whereas previous algorithms all failed to solve this (15,4)motif problem, Pevz ..."
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Cited by 285 (6 self)
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Pevzner and Sze [23] considered a precise version of the motif discovery problem and simultaneously issued an algorithmic challenge: find a motif Å of length 15, where each planted instance differs from Å in 4 positions. Whereas previous algorithms all failed to solve this (15,4)motif problem, Pevzner and Sze introduced algorithms that succeeded. However, their algorithms failed to solve the considerably more difficult (14,4), (16,5), and (18,6)motif problems. We introduce a novel motif discovery algorithm based on the use of random projections of the input’s substrings. Experiments on simulated data demonstrate that this algorithm performs better than existing algorithms and, in particular, typically solves the difficult (14,4), (16,5), and (18,6)motif problems quite efficiently. A probabilistic estimate shows that the small values of � for which the algorithm fails to recover the planted Ð � �motif are in all likelihood inherently impossible to solve. We also present experimental results on realistic biological data by identifying ribosome binding sites in prokaryotes as well as a number of known transcriptional regulatory motifs in eukaryotes. 1. CHALLENGING MOTIF PROBLEMS Pevzner and Sze [23] considered a very precise version of the motif discovery problem of computational biology, which had also been considered by Sagot [26]. Based on this formulation, they issued an algorithmic challenge: Planted Ð � �Motif Problem: Suppose there is a fixed but unknown nucleotide sequence Å (the motif) of length Ð. The problem is to determine Å, givenØ nucleotide sequences each of length Ò, and each containing a planted variant of Å. More precisely, each such planted variant is a substring that is Å with exactly � point substitutions. One instantiation that they labeled “The Challenge Problem ” was parameterized as finding a planted (15,4)motif in Ø � sequences each of length Ò � �. These values of Ò, Ø, andÐ are
Google news personalization: scalable online collaborative filtering
 in WWW, 2007
"... Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several million users and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating p ..."
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Cited by 278 (0 self)
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Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several million users and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.
Probabilistic discovery of time series motifs
, 2003
"... Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of thi ..."
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Cited by 185 (26 self)
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Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise. Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or “don’t care ” symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.
PatternHunter II: Highly Sensitive and Fast Homology Search
, 2003
"... Extending the single optimized spaced seed of PatternHunter [20] to multiple ones, PatternHunter II simultaneously remedies the lack of sensitivity of Blastn and the lack of speed of SmithWaterman, for homology search. At Blastn speed, PatternHunter II approaches SmithWaterman sensitivity, bring ..."
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Cited by 120 (12 self)
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Extending the single optimized spaced seed of PatternHunter [20] to multiple ones, PatternHunter II simultaneously remedies the lack of sensitivity of Blastn and the lack of speed of SmithWaterman, for homology search. At Blastn speed, PatternHunter II approaches SmithWaterman sensitivity, bringing homology search technology back to a full circle.
BMultiProbe LSH: Efficient indexing for highdimensional similarity search
 in Proc. 33rd Int. Conf. Very Large Data Bases
"... Similarity indices for highdimensional data are very desirable for building contentbased search systems for featurerich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate ..."
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Cited by 117 (3 self)
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Similarity indices for highdimensional data are very desirable for building contentbased search systems for featurerich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate similarity search. A significant drawback of these approaches is the requirement for a large number of hash tables in order to achieve good search quality. This paper proposes a new indexing scheme called multiprobe LSH that overcomes this drawback. Multiprobe LSH is built on the wellknown LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by and improves upon recent theoretical work on entropybased LSH designed to reduce the space requirement of the basic LSH method. We have implemented the multiprobe LSH method and evaluated the implementation with two different highdimensional datasets. Our evaluation shows that the multiprobe LSH method substantially improves upon previously proposed methods in both space and time efficiency. To achieve the same search quality, multiprobe LSH has a similar timeefficiency as the basic LSH method while reducing the number of hash tables by an order of magnitude. In comparison with the entropybased LSH method, to achieve the same search quality, multiprobe LSH uses less query time and 5 to 8 times fewer number of hash tables. 1.
Designing seeds for similarity search in genomic dna
 Journal of Computer and System Sciences
, 2003
"... Abstract: Largescale comparisons of genomic DNA are of fundamental importance in annotating functional elements in genomes. To perform large comparisons efficiently, BLAST [3, 2] and other widely used tools use seeded alignment, which compares only sequences that can be shown to share a common patt ..."
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Cited by 103 (4 self)
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Abstract: Largescale comparisons of genomic DNA are of fundamental importance in annotating functional elements in genomes. To perform large comparisons efficiently, BLAST [3, 2] and other widely used tools use seeded alignment, which compares only sequences that can be shown to share a common pattern or “seed ” of matching bases. The literature suggests that the choice of seed substantially affects the sensitivity of seeded alignment, but designing and evaluating seeds is computationally challenging. This work addresses problems arising in seed design. We give the fastest known algorithm for evaluating the sensitivity of a seed in a Markov model of ungapped alignments, as well as theoretical results on which seeds are good choices. We also describe Mandala, a software tool for seed design, and show that it can be used to improve the sensitivity of alignment in practice. 1
Nearest Neighbors In HighDimensional Spaces
, 2004
"... In this chapter we consider the following problem: given a set P of points in a highdimensional space, construct a data structure which given any query point q nds the point in P closest to q. This problem, called nearest neighbor search is of significant importance to several areas of computer sci ..."
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Cited by 93 (2 self)
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In this chapter we consider the following problem: given a set P of points in a highdimensional space, construct a data structure which given any query point q nds the point in P closest to q. This problem, called nearest neighbor search is of significant importance to several areas of computer science, including pattern recognition, searching in multimedial data, vector compression [GG91], computational statistics [DW82], and data mining. Many of these applications involve data sets which are very large (e.g., a database containing Web documents could contain over one billion documents). Moreover, the dimensionality of the points is usually large as well (e.g., in the order of a few hundred). Therefore, it is crucial to design algorithms which scale well with the database size as well as with the dimension. The nearestneighbor problem is an example of a large class of proximity problems, which, roughly speaking, are problems whose definitions involve the notion of...
Designing multiple simultaneous seeds for DNA similarity search
 in: Proc. of RECOMB’04, ACM Press, 76 – 85
, 2004
"... The challenge of similarity search in massive DNA sequence databases has inspired major changes in BLASTstyle alignment tools, which accelerate search by inspecting only pairs of sequences sharing a common short “seed, ” or pattern of matching residues. Some of these changes raise the possibility o ..."
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Cited by 75 (5 self)
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The challenge of similarity search in massive DNA sequence databases has inspired major changes in BLASTstyle alignment tools, which accelerate search by inspecting only pairs of sequences sharing a common short “seed, ” or pattern of matching residues. Some of these changes raise the possibility of improving search performance by probing sequence pairs with several distinct seeds, any one of which is sufficient for a seed match. However, designing a set of seeds to maximize their combined sensitivity to biologically meaningful sequence alignments is computationally difficult, even given recent advances in designing single seeds. This work describes algorithmic improvements to seed design that address the problem of designing a set of n seeds to be used simultaneously. We give a new local search method to optimize the sensitivity of seed sets. The method relies on efficient incremental computation of the probability that an alignment contains a match to a seed π, given that it has already failed to match any of the seeds in a set . We demonstrate experimentally that multiseed designs, even with relatively few seeds, can be significantly more sensitive than even optimized singleseed designs. Key words: DNA sequence comparison, sequence alignment, database search, seed design,