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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Fast exact k nearest neighbors search using an orthogonal search tree Presenter : Chun-Ping Wu Authors :Yi-Ching Liaw, Maw-Lin Leou, Chien-Min Wu PR 2010 國立雲林科技大學 National Yunlin University of Science and Technology 1
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation The finding process of k nearest neighbors for a query point using FSA(full search algorithm) is very time consuming. Many algorithms want to reduce the computational complexity of the kNN finding process. Pre-created tree structure 3
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation For a big PAT(Principal Axis Search), the computation time to evaluate boundary points and projection values will be large. 4
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To reduce the computation time on evaluation boundary points and projection values in the kNN searching process for a query point. The proposed method requires no boundary points and only little computation time on evaluating projection values in the kNN finding process. 5
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology The OST(orthogonal search tree) algorithm OST construction process K Nearest neighbors search using the OST 6
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology The OST construction process 7 1,2,3, 4,5,6, 7,8,9 1,2,3, 4,5,6, 7,8,9 1,2,34,5,67,8,9 1,2,3, 4,5,6, 7,8,9 1,2,34,5,67,8,9 123
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology K nearest neighbors search using the orthogonal search tree 8 1,2,3, 4,5,6, 7,8,9 1,2,34,5,67,8,9 123 456789
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Example 1 Uniform Markov source 9
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 10
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Example 2 auto-correlated data 11
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Example 3 Clustered Gaussian data 12
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Example 4 Data sets are codebook generated using 6 real images. 13
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Example 5 Statlog data set. 14 34%39%
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 15 Experimental results show that the proposed method always spends less computation time to find the kNN for a query point than the other methods. The proposed method will find the same results as those of the FSA(full search algorithm).
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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 16 Advantage To reduce the computation of the kNN finding process. Drawback Lack of illustrations Application Classification
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