Volume & Issue no: Volume 8, Issue 5, September - October 2019
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Title: |
Arabic Text Categorization based-on the Local Sparsity Ratio Mine Algorithm (LSC-mine) |
Author Name: |
Sameer Nooh, Nidal F. Shilbayeh |
Abstract: |
Abstract: Outlier detection is an important research area in
text mining, information retrieval, machine learning, and
statistics as well as enhancing natural language processing
paradigms due to the enormous numbers of new documents
being utilized for various information retrieval systems. One of
the most challenging problems in this context is addressing the
text categorization problem with Arabic text documents. In this
paper, we propose a new text categorization (TC) algorithm
which classifies Arabic text documents using the local sparsity
coefficient-mine algorithm (LSC-mine algorithm). The chosen
algorithm is capable of detecting outlier points in a spatial space
and clusters documents by computing the LSC ratio between the
new document and the cluster’s documents, which indicates the
outlier-ness of a certain point. Several experiments have been
conducted to ensure the success of the developed algorithm.
Keywords: Text Categorization, LSC-mine, Arabic
Language Text Clustering, Outlier Detection Algorithm. |
Cite this article: |
Sameer Nooh, Nidal F. Shilbayeh , "
Arabic Text Categorization based-on the Local Sparsity Ratio Mine Algorithm (LSC-mine)" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 8, Issue 5, September - October 2019 , pp.
032-036 , ISSN 2278-6856.
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