Arabic text classification based on data mining techniques
Dissertant
Thesis advisor
Hattab, Izz al-Din Shakir Hasan
Comitee Members
al-Shaykh, Asim A. R.
al-Sarayirah, Bashshar
al-Lahham, Muhammad Ismail Abd al-Rasul
University
Arab Academy for Financial and Banking Sciences
Faculty
The Faculty of Information Systems and Technology
Department
Computer information systems
University Country
Jordan
Degree
Ph.D.
Degree Date
2012
English Abstract
Classification using association rule mining also known as associative classification (AC) is a promising approach that usually builds higher accurate classifier than classic classification approaches in data mining, including probabilistic, decision trees and covering.
In this thesis, we study the problem of using AC mining in structured English and unstructured Arabic textual data collections to discover extract moderate size classifiers that end-user can easily control and understand.
The outcome is a new AC algorithm that efficiently finds knowledge (class association rules–CARs) from different classification benchmark problems, together with extensive experimental results.
Precisely, we introduce the―Reduced Classification using Association‖ (RCUA) which contains three new procedures : (1) A rule discovery procedure that uses new data representation method to efficiently discover the CARs (rules) in one data scan.
(2) A classifier building procedure that reduces the size of the classifiers and over fitting by generating a moderate size classification systems where end user can easily maintain and interpret (3) An evaluation procedure that instead of utilizing one rule for class assignment during prediction like CBA it bases its class assignment decision on multiple rules and thus improving the classification accuracy of the resulting classifiers.
During extensive experimentations using a range of real world application data (Arabic and English), we revealed that our algorithm is often more accurate than well-known traditional classification techniques and competitive with popular associative classification algorithms.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
131
Table of Contents
Table of contents.
Abstract.
Chapter One : introduction.
Chapter Two : literature review on text categorization.
Chapter Three : overview of classification based association rule discovery in data mining.
Chapter Four : new classification using association rule algorithm.
Chapter Five : conclusions and further works.
References.
American Psychological Association (APA)
al-Jamal, Muhannad Said. (2012). Arabic text classification based on data mining techniques. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-306664
Modern Language Association (MLA)
al-Jamal, Muhannad Said. Arabic text classification based on data mining techniques. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences. (2012).
https://search.emarefa.net/detail/BIM-306664
American Medical Association (AMA)
al-Jamal, Muhannad Said. (2012). Arabic text classification based on data mining techniques. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-306664
Language
English
Data Type
Arab Theses
Record ID
BIM-306664
