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Improving Clustering Accuracy using Feature Extraction Method

T. SenthilSelvi1 , R. Parimala2

  1. Department of Computer Science, Periyar E.V.R College, Trichy-23, India.
  2. Department of Computer Science, Periyar E.V.R College, Trichy-23, India.

Correspondence should be addressed to: senthilselvikumar@yahoo.co.in.


Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.15-19, Apr-2018


CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.1519


Online published on Apr 30, 2018


Copyright © T. SenthilSelvi, R. Parimala . 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: T. SenthilSelvi, R. Parimala, “Improving Clustering Accuracy using Feature Extraction Method,” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.15-19, 2018.

MLA Style Citation: T. SenthilSelvi, R. Parimala "Improving Clustering Accuracy using Feature Extraction Method." International Journal of Scientific Research in Computer Science and Engineering 6.2 (2018): 15-19.

APA Style Citation: T. SenthilSelvi, R. Parimala, (2018). Improving Clustering Accuracy using Feature Extraction Method. International Journal of Scientific Research in Computer Science and Engineering, 6(2), 15-19.

BibTex Style Citation:
@article{SenthilSelvi_2018,
author = {T. SenthilSelvi, R. Parimala},
title = {Improving Clustering Accuracy using Feature Extraction Method},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {2},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {15-19},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=600},
doi = {https://doi.org/10.26438/ijcse/v6i2.1519}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i2.1519}
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=600
TI - Improving Clustering Accuracy using Feature Extraction Method
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - T. SenthilSelvi, R. Parimala
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 15-19
IS - 2
VL - 6
SN - 2347-2693
ER -

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Abstract :
Clustering is the technique employed to group documents containing related information into clusters, which facilitates the allocation of relevant information. Clustering performance is mostly dependent on the text document features. The first challenge concerns difficulty with identifying significant term features to represent original content by considering the hidden knowledge. The second challenge is related to reducing data dimensionality without losing essential information. Clustering techniques were proposed to use feature extraction methods Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) to improve the clustering efficiency and quality. Documents are pre-processed, converted to vector space model and then clustered using the proposed algorithm. The goal of this work is to design a suitable model for clustering text document that is capable of improving clustering performance. In this paper, the problems are discussed with empirical evidence. Experimental results show that the proposed method is effective for the text clustering task.

Key-Words / Index Term :
Clustering; Euclidean Distance; Document frequency; Dimensionality reduction; Principal components

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