Abstract
A representative skyline contains k skyline points that can represent its full skyline, which is very useful in the multiple criteria decision making problems. In this paper, we focus on the distance-based representative skyline (k-DRS) query which can describe the tradeoffs among different dimensions offered by the full skyline. Since k-DRS is a NP-hard problem in d-dimensional (\(d\ge 3\)) space, it is impossible to calculate the exact k-DRS in d-dimensional space. By in-depth analyzing the properties of the k-DRS, we propose a new perspective to solve this problem and a k distance-based representative skyline algorithm based on US-ELM (DRSELM) is presented. In DRSELM, first we apply US-ELM to divide the full skyline set into k clusters. Second, in each cluster, we design a method to select a point as the representative point. Experimental results show that our DRSELM significantly outperforms its competitors in terms of both accuracy and efficiency.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472069, 61402089, 61100022 and 61173029; the National High Technology Research and Development Plan (863 Plan) under Grant No. 2012AA011004; and the Fundamental Research Funds for the Central Universities under Grant No. N130404014.
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Bai, M., Xin, J., Wang, G., Wang, X. (2016). The Distance-Based Representative Skyline Calculation Using Unsupervised Extreme Learning Machines. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_9
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DOI: https://doi.org/10.1007/978-3-319-28397-5_9
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