International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 175 - Issue 3 |
Published: Oct 2017 |
Authors: Kavitha Guda, Doolam Ramdarshan |
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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
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.