A novel approach to data mining using simplified swarm optimization

Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. However, existing traditional d...

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Main Author:	Wahid, Noorhaniza
Format:	Thesis
Language:	English
Published:	2011
Subjects:	Q Science (General) Q300-390 Cybernetics
Online Access:	http://eprints.uthm.edu.my/3067/1/24p%20NOORHANIZA%20WAHID.pdf
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id	my-uthm-ep.3067
record_format	uketd_dc
spelling	my-uthm-ep.30672021-11-02T01:47:11Z A novel approach to data mining using simplified swarm optimization 2011-01 Wahid, Noorhaniza Q Science (General) Q300-390 Cybernetics Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. However, existing traditional data classification and feature selection techniques used in data management are no longer enough for such massive data. This deficiency has prompted the need for a new intelligent data mining technique based on stochastic population-based optimization that could discover useful information from data. In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organising ability to emerge in highly distributed control problem space, and is flexible, robust and cost effective to solve complex computing environments. The proposed SSO classifier has been implemented to classify audio data. To the author’s knowledge, this is the first time that SSO and PSO have been applied for audio classification. Furthermore, two local search strategies, named Exchange Local Search (ELS) and Weighted Local Search (WLS), have been proposed to improve SSO performance. SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from the UCI repository database. Meanwhile, SSO-WLS has been implemented in Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed. The empirical analysis showed promising results with high classification accuracy rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99 datasets. Therefore, the proposed SSO rule-based classifier with local search strategies has offered a new paradigm shift in solving complex problems in data mining which may not be able to be solved by other benchmark classifiers. 2011-01 Thesis http://eprints.uthm.edu.my/3067/ http://eprints.uthm.edu.my/3067/1/24p%20NOORHANIZA%20WAHID.pdf text en public phd doctoral University of Sydney School of Information Technologies
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English
topic	Q Science (General) Q300-390 Cybernetics
spellingShingle	Q Science (General) Q300-390 Cybernetics Wahid, Noorhaniza A novel approach to data mining using simplified swarm optimization
description	Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. However, existing traditional data classification and feature selection techniques used in data management are no longer enough for such massive data. This deficiency has prompted the need for a new intelligent data mining technique based on stochastic population-based optimization that could discover useful information from data. In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organising ability to emerge in highly distributed control problem space, and is flexible, robust and cost effective to solve complex computing environments. The proposed SSO classifier has been implemented to classify audio data. To the author’s knowledge, this is the first time that SSO and PSO have been applied for audio classification. Furthermore, two local search strategies, named Exchange Local Search (ELS) and Weighted Local Search (WLS), have been proposed to improve SSO performance. SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from the UCI repository database. Meanwhile, SSO-WLS has been implemented in Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed. The empirical analysis showed promising results with high classification accuracy rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99 datasets. Therefore, the proposed SSO rule-based classifier with local search strategies has offered a new paradigm shift in solving complex problems in data mining which may not be able to be solved by other benchmark classifiers.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Wahid, Noorhaniza
author_facet	Wahid, Noorhaniza
author_sort	Wahid, Noorhaniza
title	A novel approach to data mining using simplified swarm optimization
title_short	A novel approach to data mining using simplified swarm optimization
title_full	A novel approach to data mining using simplified swarm optimization
title_fullStr	A novel approach to data mining using simplified swarm optimization
title_full_unstemmed	A novel approach to data mining using simplified swarm optimization
title_sort	novel approach to data mining using simplified swarm optimization
granting_institution	University of Sydney
granting_department	School of Information Technologies
publishDate	2011
url	http://eprints.uthm.edu.my/3067/1/24p%20NOORHANIZA%20WAHID.pdf
_version_	1747831015776190464

A novel approach to data mining using simplified swarm optimization

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