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Exploiting Database Similarity Joins for Metric Spaces
"... Similarity Joins are recognized among the most useful data processing and analysis operations and are extensively used in multiple application domains. They retrieve all data pairs whose distances are smaller than a predefined threshold ε. Multiple Similarity Join algorithms and implementation techn ..."
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Cited by 1 (0 self)
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. However, the proposed operator only support 1D numeric data. This paper presents DBSimJoin, a physical Similarity Join database operator for datasets that lie in any metric space. DBSimJoin is a nonblocking operator that prioritizes the early generation of results. We implemented the proposed operator
Title: Optimizing Data Page Placement for Similarity Join in Metric Spaces
"... In this paper, we address the similarity join problem in metric space. We describe how page locality may be improved by reorganizing disk pages using an algorithm borrowed from numerical methods concerning the reorganization of the rows and columns of matrices to form banded matrices. The model is f ..."
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In this paper, we address the similarity join problem in metric space. We describe how page locality may be improved by reorganizing disk pages using an algorithm borrowed from numerical methods concerning the reorganization of the rows and columns of matrices to form banded matrices. The model
List of twin clusters: a data structure for similarity joins in metric spaces
 In Proc. 1st Intl. Workshop on Similarity Search and Applications (SISAP’08
, 2008
"... The metric space model abstracts many proximity or similarity problems, where the most frequently considered primitives are range and knearest neighbor search, leaving out the similarity join, an extremely important primitive. In fact, despite the great attention that this primitive has received in ..."
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Cited by 1 (1 self)
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The metric space model abstracts many proximity or similarity problems, where the most frequently considered primitives are range and knearest neighbor search, leaving out the similarity join, an extremely important primitive. In fact, despite the great attention that this primitive has received
A ContentAddressable Network for Similarity Join in Metric Spaces ∗
"... Similarity join is an interesting complement of the wellestablished similarity range and nearest neighbors search primitives in metric spaces. However, the quadratic computational complexity of similarity join prevents from applications on large data collections. We present MCAN+, an extension of ..."
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Similarity join is an interesting complement of the wellestablished similarity range and nearest neighbors search primitives in metric spaces. However, the quadratic computational complexity of similarity join prevents from applications on large data collections. We present MCAN+, an extension
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 652 (38 self)
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A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
Searching in metric spaces
, 2001
"... The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather gen ..."
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Cited by 432 (38 self)
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general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without crossknowledge. Because of this, the same ideas have been reconceived several times, and very different
Gradient flows in metric spaces and in the space of probability measures
 LECTURES IN MATHEMATICS ETH ZÜRICH, BIRKHÄUSER VERLAG
, 2005
"... ..."
The Similarity Metric
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2003
"... A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new "normalized information distance", based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class ..."
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Cited by 276 (34 self)
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and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the similarity metric. This theory forms the foundation for a new practical tool. To evidence generality and robustness we give two
Efficient similarity search in sequence databases
, 1994
"... We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong. Anot ..."
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Cited by 505 (21 self)
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We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong
Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the us ..."
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Cited by 799 (14 self)
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be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider "similar." For instance, we may ask them to provide
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