Comparison of Different Distance Measure Methods in Text Document Clustering

International Journal of Research and Engineering

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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
Peer-reviewed Article
Format application/pdf
Source International Journal of Research and Engineering; Vol 5 No 7 (2018): July 2018 Edition; 445-449
Language eng
Rights Copyright (c) 2018 Yin Min Tun

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