Show simple item record

dc.contributor.advisorDahal, Keshav P.
dc.contributor.authorAbdo, Walid A.A.*
dc.date.accessioned2013-11-04T11:15:59Z
dc.date.available2013-11-04T11:15:59Z
dc.date.issued2013-11-04
dc.identifier.urihttp://hdl.handle.net/10454/5661
dc.description.abstractOver the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectAssociation rulesen_US
dc.subjectData miningen_US
dc.subjectDistributed systemsen_US
dc.subjectDecision support systemsen_US
dc.subjectMulti-Agent systemsen_US
dc.subjectIntelligent data analysisen_US
dc.subjectDistributed Multi-Agent Association Rules algorithm (DMAAR)en_US
dc.subjectBitTable Multi-Agent Association Rules algorithm (BMAAR)en_US
dc.subjectPruning Multi-Agent Association Rules algorithm (PMAAR)en_US
dc.subjectMedical databasesen_US
dc.subjectClinical decision makingen_US
dc.titleEnhancing association rules algorithms for mining distributed databases. Integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support.en_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentDepartment of Computingen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2012
refterms.dateFOA2018-07-19T11:58:30Z


Item file(s)

Thumbnail
Name:
Final Thesis 2012-11-06 - with ...
Size:
2.991Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record