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Document Object Mapping and Clustering Using Semantic Indexing Process

V. Geetha1 , C. Vivekeswari2

Section:Survey Paper, Product Type: Journal Paper
Volume-07 , Issue-04 , Page no. 370-374, Feb-2019

Online published on Feb 28, 2019

Copyright © V. Geetha, C. Vivekeswari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: V. Geetha, C. Vivekeswari, “Document Object Mapping and Clustering Using Semantic Indexing Process,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.370-374, 2019.

MLA Style Citation: V. Geetha, C. Vivekeswari "Document Object Mapping and Clustering Using Semantic Indexing Process." International Journal of Computer Sciences and Engineering 07.04 (2019): 370-374.

APA Style Citation: V. Geetha, C. Vivekeswari, (2019). Document Object Mapping and Clustering Using Semantic Indexing Process. International Journal of Computer Sciences and Engineering, 07(04), 370-374.

BibTex Style Citation:
@article{Geetha_2019,
author = {V. Geetha, C. Vivekeswari},
title = {Document Object Mapping and Clustering Using Semantic Indexing Process},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {07},
Issue = {04},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {370-374},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=793},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=793
TI - Document Object Mapping and Clustering Using Semantic Indexing Process
T2 - International Journal of Computer Sciences and Engineering
AU - V. Geetha, C. Vivekeswari
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 370-374
IS - 04
VL - 07
SN - 2347-2693
ER -

           

Abstract

Document clustering aims to automatically group related documents into clusters. It is on of the most important tasks in machine learning and artificial intelligence and has received much attention in recent years During this framework, the documents are projected into a low-dimensional semantic area during which the correlations between the documents within the native patches are maximized whereas the correlations between the documents outside these patches are minimized simultaneously. Since the intrinsic geometrical structure of the document area is usually embedded within the similarities between the documents, correlation as a similarity live is additional appropriate for detecting the intrinsic geometrical structure of the document area than Euclidean distance. Consequently, the proposed CPI technique will effectively discover the intrinsic structures embedded in high-dimensional document area. The effectiveness of the new technique is demonstrated by in depth experiments conducted on varied information sets and by comparison with existing document clustering strategies.

Key-Words / Index Term

Document clustering, correlation measure, correlation latent semantic indexing dimensionality reduction

References

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