Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach tha...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 35; číslo 1; s. 110 - 122
Hlavní autori:	Özyer, Tansel, Zhang, Ming, Alhajj, Reda
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.08.2011 Springer Nature B.V
Predmet:	Artificial Intelligence Clustering Computation Computer Science Decision making Genetic algorithms Intelligence Machines Manufacturing Mechanical Engineering Objectives Optimization Pareto optimum Partitioning Processes Tasks Skyline computation Multi-objective clustering Cluster validation Genetic algorithm
ISSN:	0924-669X, 1573-7497
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Abstract	Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.
AbstractList	Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments. Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.[PUBLICATION ABSTRACT]
Author	Alhajj, Reda Özyer, Tansel Zhang, Ming
Author_xml	– sequence: 1 givenname: Tansel surname: Özyer fullname: Özyer, Tansel organization: Department of Computer Engineering, TOBB ETU Economics and Technology University – sequence: 2 givenname: Ming surname: Zhang fullname: Zhang, Ming organization: Department of Computer Science, University of Calgary – sequence: 3 givenname: Reda surname: Alhajj fullname: Alhajj, Reda email: alhajj@ucalgary.ca organization: Department of Computer Science, University of Calgary, Department of Computer Science, Global University
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Snippet	Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values...
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SubjectTerms	Artificial Intelligence Clustering Computation Computer Science Decision making Genetic algorithms Intelligence Machines Manufacturing Mechanical Engineering Objectives Optimization Pareto optimum Partitioning Processes Tasks
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Title	Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation
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