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eBook High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity Searches (Lecture Notes in Computer Science) ePub

by Cui Yu

eBook High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity Searches (Lecture Notes in Computer Science) ePub
Author: Cui Yu
Language: English
ISBN: 3540441999
ISBN13: 978-3540441991
Publisher: Springer; 2002 edition (December 16, 2002)
Pages: 161
Category: Networking & Cloud Computing
Subcategory: Computers
Rating: 4.5
Votes: 340
Formats: txt lit mobi rtf
ePub file: 1202 kb
Fb2 file: 1998 kb

In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for .

In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed. Performance Study of Similarity Queries.

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Advances in Computing Science-ASIAN 2002 : Internet Computing and Modeling, Grid Computing, Peer-to-Peer Computing, and Cluster Computing: 7th Asian Computing Science Conference, Hanoi, Vietnam, December 4-6, 2002, Proceedings. Jean-Marie, Alain, Ed.

While many approaches have been developed in isolation, quite a few have been.

We tackle three crucial issues in high-dimensional time-series data clustering for pattern discovery: appropriate similarity measures .

We tackle three crucial issues in high-dimensional time-series data clustering for pattern discovery: appropriate similarity measures, efficient procedures for high-dimensional setting, and fast/scalable clustering algorithms.

High-dimensional signal recovery problems Why this book? Data sciences are moving fast, and probabilistic methods often .

High-dimensional signal recovery problems. Signal recovery based on M bound. Recovery of sparse signals. Why this book? Data sciences are moving fast, and probabilistic methods often provide a foun-dation and inspiration for such advances. What is this book about? High-dimensional probability is an area of probability theory that studies random objects in Rn where the dimension n can be very large.

Lecture Notes in Computer Science is a series of computer science books published by Springer Science+Business Media since 1973. The series contains proceedings, post-proceedings, and monographs. In addition, tutorials, state-of-the-art surveys, and "hot topics" are increasingly being included. Two sub-series are: Lecture Notes in Artificial Intelligence. Lecture Notes in Bioinformatics. Monographiae Biologicae, another monograph series published by Springer Science+Business Media.

Evolutionary approaches to visualisation and knowledge discovery Russell . THEN term OP valuerange (AN. where term is a class from the dataset, OP is one of the standard comparison operators (<.

Evolutionary approaches to visualisation and knowledge discovery Russell Beale, Andy Pryke and . Hendley School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, UK {. eale, . uk/{~rxb, ~anp, ~rjh} Abstract. The similarity metric should match an intuitive view of the similarity of two items. In most cases, a simple and standard distance measure performs well. value is a numeric or symbolic value, and range is a numeric range.

ISSN 1611-3349 (electronic). Lecture Notes in Computer Science. LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues. ISBN 978-3-319-94666-5. ISBN 978-3-319-94667-2 (eBook). Library of Congress Control Number: 2018939453. The technical program was nalized by selecting the highest-quality papers from among 69 submitted papers.

In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed indexing methods.Many new database applications, such as multimedia databases or stock price information systems, transform important features or properties of data objects into high-dimensional points. Searching for objects based on these features is thus a search of points in this feature space. To support efficient retrieval in such high-dimensional databases, indexes are required to prune the search space. Indexes for low-dimensional databases are well studied, whereas most of these application specific indexes are not scaleable with the number of dimensions, and they are not designed to support similarity searches and high-dimensional joins.
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