Comparison of Different Distance Measure Methods in Text Document Clustering

International Journal of Research and Engineering

View Publication Info
 
 
Field Value
 
Title Comparison of Different Distance Measure Methods in Text Document Clustering
 
Creator Tun, Yin Min
 
Description Clustering text document is an unsupervised learning method to find common groups. The clustering of text documents are the special issue in text mining for unlabeled train documents. Fortunately, there are many proposed features and methods to resolve this problem. The framework of text document classification consists of: input text document, preprocessing, feature extraction and clustering. The common classification methods are: self-organization map, k-means and mixture of Gaussians. The correlation of resulted clusters is based on selecting a distance measure method. The main focus of this paper is to present different exiting distance measure methods along with k-means clustering for text document clustering. The experiment performed k-means clustering on the Newsgroups dataset and measure clustering entropy to evaluate the different distance measure methods.
 
Publisher IJRE Publisher
 
Date 2018-08-07
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/347
10.21276/ijre.2018.5.7.2
 
Source International Journal of Research and Engineering; Vol 5 No 7 (2018): July 2018 Edition; 445-449
2348-7860
2348-7852
 
Language eng
 
Relation https://digital.ijre.org/index.php/int_j_res_eng/article/view/347/318
 
Rights Copyright (c) 2018 Yin Min Tun
http://creativecommons.org/licenses/by/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library