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Nearest Keyword Multi-Dimensional Data by Index Hashing

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Kavitha Guda, Doolam Ramdarshan
10.5120/ijca2017915478

Kavitha Guda and Doolam Ramdarshan. Nearest Keyword Multi-Dimensional Data by Index Hashing. International Journal of Computer Applications 175(3):13-15, October 2017. BibTeX

@article{10.5120/ijca2017915478,
	author = {Kavitha Guda and Doolam Ramdarshan},
	title = {Nearest Keyword Multi-Dimensional Data by Index Hashing},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2017},
	volume = {175},
	number = {3},
	month = {Oct},
	year = {2017},
	issn = {0975-8887},
	pages = {13-15},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume175/number3/28468-2017915478},
	doi = {10.5120/ijca2017915478},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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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Keywords

Clustering, Filtering, Multi-dimensional data, Indexing, Hashing