Research Article

Nearest Keyword Multi-Dimensional Data by Index Hashing

by  Kavitha Guda, Doolam Ramdarshan
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Issue 3
Published: Oct 2017
Authors: Kavitha Guda, Doolam Ramdarshan
10.5120/ijca2017915478
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Kavitha Guda, Doolam Ramdarshan . Nearest Keyword Multi-Dimensional Data by Index Hashing. International Journal of Computer Applications. 175, 3 (Oct 2017), 13-15. DOI=10.5120/ijca2017915478

                        @article{ 10.5120/ijca2017915478,
                        author  = { Kavitha Guda,Doolam Ramdarshan },
                        title   = { Nearest Keyword Multi-Dimensional Data by Index Hashing },
                        journal = { International Journal of Computer Applications },
                        year    = { 2017 },
                        volume  = { 175 },
                        number  = { 3 },
                        pages   = { 13-15 },
                        doi     = { 10.5120/ijca2017915478 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2017
                        %A Kavitha Guda
                        %A Doolam Ramdarshan
                        %T Nearest Keyword Multi-Dimensional Data by Index Hashing%T 
                        %J International Journal of Computer Applications
                        %V 175
                        %N 3
                        %P 13-15
                        %R 10.5120/ijca2017915478
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Catchphrase predicated look for in content prosperous multi-dimensional datasets encourages various novel applications and executes. In this paper, we consider objects that are marked with catchphrases and are embedded in a vector space. For these datasets, we ponder request that demand the most impervious aggregations of centers slaking a given course of action of watchwords. We propose a novel strategy called ProMiSH (Projection and Multi Scale Hashing) that uses self-confident projection and hash-predicated list structures, and achieves high flexibility and speedup. We present a right and an estimated variation of the count. Our exploratory results on sound and produced datasets show that ProMiSH has up to 60 times of speedup over front line tree-predicated frameworks.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Clustering Filtering Multi-dimensional data Indexing Hashing

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