Using rough set through for classification of image segmentation data

Knowledge Discovery in Database (KDD) can be defined as a technology or a process that helps to extract valuable information including hidden and unseen patterns, trends and relationships between variables from a large amount of data. The information learnt and the discovery made can help in applyin...

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Main Author:	Diallo Abdoulaye, Kindy
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/48667/1/DialloAbdoulayeKindyMAIS2014.pdf
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id	my-utm-ep.48667
record_format	uketd_dc
spelling	my-utm-ep.486672020-06-16T08:00:53Z Using rough set through for classification of image segmentation data 2014 Diallo Abdoulaye, Kindy QA Mathematics Knowledge Discovery in Database (KDD) can be defined as a technology or a process that helps to extract valuable information including hidden and unseen patterns, trends and relationships between variables from a large amount of data. The information learnt and the discovery made can help in applying the new found pattern in the training set to an unseen data, known as test set, that can guide and facilitate a crucial business decision making task. A large number of data mining techniques have been proposed in the literature for classification purpose. In this work, we are using the Rough Set Classifier (RSC) for mining image segmentation data set obtained from an online machine learning data repository. The RSC is a rule based data mining technique which generates rules from large databases and has great capabilities to deal with noise and uncertainty in data set. In order to find out the best accuracy method, we conducted around 10 experiments by varying the proportions between the training and test sets. The best method gave us an accuracy of 85.71%. 2014 Thesis http://eprints.utm.my/id/eprint/48667/ http://eprints.utm.my/id/eprint/48667/1/DialloAbdoulayeKindyMAIS2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:82003?queryType=vitalDismax&query=Using+rough+set+through+for+classification+of+image+segmentation+data&public=true masters Universiti Teknologi Malaysia, Advanced Informatics School Advanced Informatics School
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Diallo Abdoulaye, Kindy Using rough set through for classification of image segmentation data
description	Knowledge Discovery in Database (KDD) can be defined as a technology or a process that helps to extract valuable information including hidden and unseen patterns, trends and relationships between variables from a large amount of data. The information learnt and the discovery made can help in applying the new found pattern in the training set to an unseen data, known as test set, that can guide and facilitate a crucial business decision making task. A large number of data mining techniques have been proposed in the literature for classification purpose. In this work, we are using the Rough Set Classifier (RSC) for mining image segmentation data set obtained from an online machine learning data repository. The RSC is a rule based data mining technique which generates rules from large databases and has great capabilities to deal with noise and uncertainty in data set. In order to find out the best accuracy method, we conducted around 10 experiments by varying the proportions between the training and test sets. The best method gave us an accuracy of 85.71%.
format	Thesis
qualification_level	Master's degree
author	Diallo Abdoulaye, Kindy
author_facet	Diallo Abdoulaye, Kindy
author_sort	Diallo Abdoulaye, Kindy
title	Using rough set through for classification of image segmentation data
title_short	Using rough set through for classification of image segmentation data
title_full	Using rough set through for classification of image segmentation data
title_fullStr	Using rough set through for classification of image segmentation data
title_full_unstemmed	Using rough set through for classification of image segmentation data
title_sort	using rough set through for classification of image segmentation data
granting_institution	Universiti Teknologi Malaysia, Advanced Informatics School
granting_department	Advanced Informatics School
publishDate	2014
url	http://eprints.utm.my/id/eprint/48667/1/DialloAbdoulayeKindyMAIS2014.pdf
_version_	1747817446382764032

Using rough set through for classification of image segmentation data

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