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An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures

U.S. Patki1 , A.B. Kurhe2 , P.G. Khot3

  1. Dept. of Computer Science, NES Science College, Nanded, India.
  2. Dept. of Computer Science, SGBS College Purna(Jn), India.
  3. Dept. of Statistics, RSTM University, Nagpur, India.

Correspondence should be addressed to: patkiulhas@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 79-83, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.7983

Online published on Dec 31, 2017

Copyright © U.S. Patki, A.B. Kurhe, P.G. Khot . 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: U.S. Patki, A.B. Kurhe, P.G. Khot, “An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.79-83, 2017.

MLA Style Citation: U.S. Patki, A.B. Kurhe, P.G. Khot "An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures." International Journal of Computer Sciences and Engineering 5.12 (2017): 79-83.

APA Style Citation: U.S. Patki, A.B. Kurhe, P.G. Khot, (2017). An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures. International Journal of Computer Sciences and Engineering, 5(12), 79-83.

BibTex Style Citation:
@article{Patki_2017,
author = {U.S. Patki, A.B. Kurhe, P.G. Khot},
title = {An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2017},
volume = {5},
Issue = {12},
month = {12},
year = {2017},
issn = {2347-2693},
pages = {79-83},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1584},
doi = {https://doi.org/10.26438/ijcse/v5i12.7983}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i12.7983}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1584
TI - An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures
T2 - International Journal of Computer Sciences and Engineering
AU - U.S. Patki, A.B. Kurhe, P.G. Khot
PY - 2017
DA - 2017/12/31
PB - IJCSE, Indore, INDIA
SP - 79-83
IS - 12
VL - 5
SN - 2347-2693
ER -

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Abstract

Searching is the most important process in an information retrieval from available large databases. Many times we search for a set of documents which is relevant to the given search document. Text mining helps us to mine the information from a given set of documents and it is most popular technique in Information retrieval. In this research paper we have applied distinct distance measures for retrieval of most similar documents to the queried document from a set of given document. For obtaining optimality for required search, we have gone through pre-processing of documents, creating vector space model and used distance measure techniques. Precision and recall are the basic measures used in evaluating search strategies. We have presented five distance measure technique applied on hundred text documents from standard database 20NewsGroup and calculated Recall and precision rate for text documents retrieval. We have used MatLab 10a as a development tool for our experiment.

Key-Words / Index Term

Text Mining, Information retrieval, distance measure, recall rate, precession rate, document

References

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[4]A. Sudha Ramkumar et. al, “Text Document Clustering Using Dimension Reduction Technique”, IJAER Vol -11, November 7, 2016,
[5] A. Singh, et.al ,”K-means with Three different Distance Metrics”, IJCA, (0975 – 8887) Volume 67– No.10, April 2013
[6] A.Huang, ,” Similarity Measures for Text Document Clustering”, NZCSRSC 2008, April 2008, Christchurch, New Zealand
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