A modified multi-class association rule for text mining

Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of ru...

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Main Author:	Al-Refai, Mohammad Hayel Abdel Karim
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/5767/1/depositpermission_s91487.pdf https://etd.uum.edu.my/5767/2/s91487_01.pdf
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id	my-uum-etd.5767
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Yusof, Yuhanis
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Al-Refai, Mohammad Hayel Abdel Karim A modified multi-class association rule for text mining
description	Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of rules produced in order to obtain high classification accuracy. This study proposes s a Modified Multi-class Association Rule Mining (mMCAR) that consists of three procedures; rule discovery, rule pruning and group-based class assignment. The rule discovery and rule pruning procedures are designed to reduce the number of classification rules. On the other hand, the group-based class assignment procedure contributes in improving the classification accuracy. Experiments on the structured and unstructured text datasets obtained from the UCI and Reuters repositories are performed in order to evaluate the proposed Association Classifier. The proposed mMCAR classifier is benchmarked against the traditional classifiers and existing Association Classifiers. Experimental results indicate that the proposed Association Classifier, mMCAR, produced high accuracy with a smaller number of classification rules. For the structured dataset, the mMCAR produces an average of 84.24% accuracy as compared to MCAR that obtains 84.23%. Even though the classification accuracy difference is small, the proposed mMCAR uses only 50 rules for the classification while its benchmark method involves 60 rules. On the other hand, mMCAR is at par with MCAR when unstructured dataset is utilized. Both classifiers produce 89% accuracy but mMCAR uses less number of rules for the classification. This study contributes to the text mining domain as automatic classification of huge and widely distributed textual data could facilitate the text representation and retrieval processes.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Al-Refai, Mohammad Hayel Abdel Karim
author_facet	Al-Refai, Mohammad Hayel Abdel Karim
author_sort	Al-Refai, Mohammad Hayel Abdel Karim
title	A modified multi-class association rule for text mining
title_short	A modified multi-class association rule for text mining
title_full	A modified multi-class association rule for text mining
title_fullStr	A modified multi-class association rule for text mining
title_full_unstemmed	A modified multi-class association rule for text mining
title_sort	modified multi-class association rule for text mining
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5767/1/depositpermission_s91487.pdf https://etd.uum.edu.my/5767/2/s91487_01.pdf
_version_	1747827978770841600
spelling	my-uum-etd.57672016-07-20T10:01:45Z A modified multi-class association rule for text mining 2015 Al-Refai, Mohammad Hayel Abdel Karim Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of rules produced in order to obtain high classification accuracy. This study proposes s a Modified Multi-class Association Rule Mining (mMCAR) that consists of three procedures; rule discovery, rule pruning and group-based class assignment. The rule discovery and rule pruning procedures are designed to reduce the number of classification rules. On the other hand, the group-based class assignment procedure contributes in improving the classification accuracy. Experiments on the structured and unstructured text datasets obtained from the UCI and Reuters repositories are performed in order to evaluate the proposed Association Classifier. The proposed mMCAR classifier is benchmarked against the traditional classifiers and existing Association Classifiers. Experimental results indicate that the proposed Association Classifier, mMCAR, produced high accuracy with a smaller number of classification rules. For the structured dataset, the mMCAR produces an average of 84.24% accuracy as compared to MCAR that obtains 84.23%. Even though the classification accuracy difference is small, the proposed mMCAR uses only 50 rules for the classification while its benchmark method involves 60 rules. On the other hand, mMCAR is at par with MCAR when unstructured dataset is utilized. Both classifiers produce 89% accuracy but mMCAR uses less number of rules for the classification. This study contributes to the text mining domain as automatic classification of huge and widely distributed textual data could facilitate the text representation and retrieval processes. 2015 Thesis https://etd.uum.edu.my/5767/ https://etd.uum.edu.my/5767/1/depositpermission_s91487.pdf text eng staffonly https://etd.uum.edu.my/5767/2/s91487_01.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia [1] U. Fayyad, et al., "From data mining to knowledge discovery in databases," AI magazine, vol. 17, p. 37, 1996. [2] I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann Pub, 2005. [3] W. Han, et al., "Research on the Problem Model of GUI based on Knowledge Discovery in Database," in 2013 International Conference on Software Engineering and Computer Science, 2013. [4] A. 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A modified multi-class association rule for text mining

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