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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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 35; H. 1; S. 110 - 122
Hauptverfasser: Özyer, Tansel, Zhang, Ming, Alhajj, Reda
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.08.2011
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-009-0206-7