Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6907
Title: Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation
Authors: Ozyer, Tansel
Zhang, Ming
Alhajj, Reda
Keywords: Skyline computation
Multi-objective clustering
Genetic algorithm
Cluster validation
Issue Date: 2011
Publisher: Springer
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.
URI: https://doi.org/10.1007/s10489-009-0206-7
https://hdl.handle.net/20.500.11851/6907
ISSN: 0924-669X
1573-7497
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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