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136
Reverse Nearest Neighbor Search in Metric Spaces
 TKDE
"... Abstract—Given a set D of objects, a reverse nearest neighbor (RNN) query returns the objects o in D such that o is closer to a query object q than to any other object in D, according to a certain similarity metric. The existing RNN solutions are not sufficient because they either 1) rely on precomp ..."
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Cited by 25 (1 self)
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on precomputed information that is expensive to maintain in the presence of updates or 2) are applicable only when the data consists of “Euclidean objects ” and similarity is measured using the L2 norm. In this paper, we present the first algorithms for efficient RNN search in generic metric spaces. Our
Efficient Reverse kNearest Neighbor Search in Arbitrary Metric Spaces
, 2006
"... The reverse knearest neighbor (RkNN) problem, i.e. finding all objects in a data set the knearest neighbors of which include a specified query object, is a generalization of the reverse 1nearest neighbor problem which has received increasing attention recently. Many industrial and scientific appl ..."
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Cited by 34 (9 self)
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but not for general metric objects. In this paper, we propose the first approach for efficient RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use conservative and progressive distance
On the Surprising Behavior of Distance Metrics in High Dimensional Space
 Lecture Notes in Computer Science
, 2001
"... In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a efficienc ..."
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Cited by 200 (2 self)
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In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a
iDistance: An Adaptive B+tree Based Indexing Method for Nearest Neighbor Search
, 2005
"... In this article, we present an efficient B +tree based indexing method, called iDistance, for Knearest neighbor (KNN) search in a highdimensional metric space. iDistance partitions the data based on a space or datapartitioning strategy, and selects a reference point for each partition. The data ..."
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Cited by 93 (10 self)
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In this article, we present an efficient B +tree based indexing method, called iDistance, for Knearest neighbor (KNN) search in a highdimensional metric space. iDistance partitions the data based on a space or datapartitioning strategy, and selects a reference point for each partition
Finding Nearest Neighbors in Growthrestricted Metrics
"... Most research on nearest neighbor algorithms in the literature has been focused on the Euclidean case. In many practical search problems however, the underlying metric is nonEuclidean. Nearest neighbor algorithms for general metric spaces are quite weak, which motivates a search for other classes o ..."
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of metric spaces that can be tractably searched. In this paper, we develop an efficient dynamic data structure for nearest neighbor queries in growthconstrained metrics. These metrics satisfy the property that for any point q and distance d the number of points within distance 2d of q is at most a constant
Finding Nearest Neighbors in Growthrestricted Metrics
"... Most research on nearest neighbor algorithms in the literature has been focused on the Euclidean case. In many practical search problems however, the underlying metric is nonEuclidean. Nearest neighbor algorithms for general metric spaces are quite weak, which motivates a search for other classes o ..."
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of metric spaces that can be tractably searched. In this paper, we develop an efficient dynamic data structure for nearest neighbor queries in growthconstrained metrics. These metrics satisfy the property that for any point q and distance d the number of points within distance 2d of q is at most a constant
Finding Nearest Neighbors in Growthrestricted Metrics
"... Most research on nearest neighbor algorithms in the literature has been focused on the Euclidean case. In many practical search problems however, the underlying metric is nonEuclidean. Nearest neighbor algorithms for general metric spaces are quite weak, which motivates a search for other classes o ..."
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of metric spaces that can be tractably searched. In this paper, we develop an efficient dynamic data structure for nearest neighbor queries in growthconstrained metrics. These metrics satisfy the property that for any point q and number r the ratio between numbers of points in balls of radius 2r and r
A Road Network Embedding Technique for kNearest Neighbor Search in Moving Object Databases
 GeoInformatica
, 2002
"... A very important class of queries in GIS applications is the class of Knearest neighbor queries. Most of the current studies on the Knearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In realworld road networks, however, th ..."
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Cited by 90 (5 self)
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A very important class of queries in GIS applications is the class of Knearest neighbor queries. Most of the current studies on the Knearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In realworld road networks, however
TrinaryProjection Trees for Approximate Nearest Neighbor Search
"... Abstract—We address the problem of approximate nearest neighbor (ANN) search for visual descriptor indexing. Most spatial partition trees, such as KD trees, VP trees and so on, follow the hierarchical binary space partitioning framework. The key effort is to design different partition functions (hyp ..."
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Cited by 4 (2 self)
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Abstract—We address the problem of approximate nearest neighbor (ANN) search for visual descriptor indexing. Most spatial partition trees, such as KD trees, VP trees and so on, follow the hierarchical binary space partitioning framework. The key effort is to design different partition functions
Efficient Reverse kNearest Neighbor Estimation
, 2007
"... The reverse knearest neighbor (RkNN) problem, i.e. finding all objects in a data set the knearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric ..."
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Cited by 4 (3 self)
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approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearestneighbordistances in order to prune the search space. We show that our method scales significantly better than existing nonapproximative approaches while producing an approximation
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