Improving Kurdish Web Mining through Tree Data Structure and Porter’s Stemmer Algorithms




Kurdish text classification, Porter's stemmer algorithm, Stemming, Tree data structure


Stemming is one of the main important preprocessing techniques that can be used to enhance the accuracy of text classification. The key purpose of using the stemming is combining the number of words that have same stem to decrease high dimensionality of feature space. Reducing feature space cause to decline time to construct a model and minimize the memory space. In this paper, a new stemming approach is explored for enhancing Kurdish text classification performance. Tree data structure and Porter’s stemmer algorithms are incorporated for building the proposed approach.  The system is assessed through using Support Vector Machine (SVM) and Decision Tree (C4.5) to illustrate the performance of the suggested stemmer after and before applying it. Furthermore, the usefulness of using stop words are considered before and after implementing the suggested approach.


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Author Biographies

  • Ari M. Saeed, Department of Computer Science, College of Science, University of Halabja, Kurdistan Region - F.R. Iraq


    Ari M. Saeed is an Assistant Lecturer and a Researcher at Halabja University, College of Science, computer Department. He holds M.Sc. in computer engineering at Lefke University, Cyprus in 2013- 2015. He interests also include Machine learning and Artificial intelligent.

  • Tarik A. Rashid, Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Kurdistan Region - F.R. Iraq

    Dr. Tarik Ahmed Rashid received his Ph.D. in Computer Science and Informatics degree from College of Engineering, Mathematical and Physical Sciences, University College Dublin (UCD) in 2001-2006. He pursued his Post-Doctoral Follow at the Computer Science and Informatics School, College of Engineering, Mathematical and Physical Sciences, University College Dublin (UCD) from 2006-2007. He was a Professor at Salahaddin University-Erbil, Hawler, Kurdistan. He Joined the University of Kurdistan Hewlêr (UKH) in 2017.

  • Arazo M. Mustafa, College of Basic Education, University of Kirkuk, F.R. Iraq


    Currently, Arazo Mohamed Mustafa is a Lecturer at the College of Basic Education in the University of Kirkuk, Iraq. Arazo Mohamed Mustafa has a B.Sc. in Computer Sciences from the University of Kirkuk, Iraq (2007) and MS.c degree in Computer Science from University of Sulaimani in Kurdistan Region, Iraq (2017). From 2008 to 2013 Arazo Mohamed Mustafa worked as a software engineer at the Directorate of Municipalities of Kirkuk in the Projects Department of the Ministry of Construction, Housing and Public Municipalities. Her research interests are in the areas of Artificial Intelligence, Programming Languages, Pattern recognition, Machine Learning, and Data Analysis.

  • Polla Fattah, Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Kurdistan Region - F.R. Iraq


    Polla Fattah is a Assistant Lecturer at Salahaddin University-Erbi and a visiting lecturer at Kurdistan Unievrsity-Hawler. He is interested in data mining, machine learning and optimizations problems especially time series analysis and deep learning. 

  • Birzo Ismael, UKH, Computer Science and Engineering


    Birzo Ismael holds B.Sc. degree in Computer Science from Kingston University in London, and M.Sc. degree in Software Engineering from the same university. He joined the department of Computer Sciences and Engineering in Sep 2016. Birzo’s history with UKH goes back to June 2011, when he first joined UKH as a Software developer; later in April 2013 he took up the role of the Director of IT Admin. until he finally joined CSE department as a lecturer in Sep 2016.


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Research Articles

How to Cite

Improving Kurdish Web Mining through Tree Data Structure and Porter’s Stemmer Algorithms. (2018). UKH Journal of Science and Engineering, 2(1), 48-54.