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A Clustering Framework for Large Document Datasets

K.K. Mohbey1 , G.S. Thakur2

Section:Research Paper, Product Type: Journal Paper
Volume-1 , Issue-1 , Page no. 26-30, Sep-2013

Online published on Sep 30, 2013

Copyright © K.K. Mohbey, G.S. Thakur . 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: K.K. Mohbey, G.S. Thakur , “A Clustering Framework for Large Document Datasets,” International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.26-30, 2013.

MLA Style Citation: K.K. Mohbey, G.S. Thakur "A Clustering Framework for Large Document Datasets." International Journal of Computer Sciences and Engineering 1.1 (2013): 26-30.

APA Style Citation: K.K. Mohbey, G.S. Thakur , (2013). A Clustering Framework for Large Document Datasets. International Journal of Computer Sciences and Engineering, 1(1), 26-30.

BibTex Style Citation:
@article{Mohbey_2013,
author = {K.K. Mohbey, G.S. Thakur },
title = {A Clustering Framework for Large Document Datasets},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2013},
volume = {1},
Issue = {1},
month = {9},
year = {2013},
issn = {2347-2693},
pages = {26-30},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=11},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=11
TI - A Clustering Framework for Large Document Datasets
T2 - International Journal of Computer Sciences and Engineering
AU - K.K. Mohbey, G.S. Thakur
PY - 2013
DA - 2013/09/30
PB - IJCSE, Indore, INDIA
SP - 26-30
IS - 1
VL - 1
SN - 2347-2693
ER -

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Abstract

Document set is the collection of different types of document. Each document contains special type of information, which is beneficial for the peoples. We have the need of document clustering by their similarity. Document may contain data related to the blogs, website access pattern, any transaction or simply text. By the clustering of similar documents one can find the future trends of the people and it is also useful for the business point of view. In this paper, we have proposed a clustering approach for large size document sets. This proposed approach immediately assign document into appropriate cluster. Experiments are conducted with the twenty newsgroup dataset using java and MATLAB software. Comparisons are also performed with the existing methods. Experimental results show the effectiveness of the proposed approach for large document sets.

Key-Words / Index Term

Large Document Set, Similarity measurement, Term Extraction, Dendrogram

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