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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50853

Title: Fast agglomerative clustering using information of k-nearest neighbors
Authors: Chih-Tang Chang
Jim Z.C.Lai
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Nearest neighbor
Agglomerative clustering
Vector quantization
Date: 2010-12
Issue Date: 2018-10-29T02:03:40Z
Publisher: Pattern Recognition
Abstract: Abstract: In this paper, we develop a method to lower the computational complexity of pairwise nearest neighbor (PNN) algorithm. Our approach determines a set of candidate clusters being updated after each cluster merge. If the updating process is required for some of these clusters, k-nearest neighbors are found for them. The number of distance calculations for our method is O(N2), where N is the number of data points. To further reduce the computational complexity of the proposed algorithm, some available fast search approaches are used. Compared to available approaches, our proposed algorithm can reduce the computing time and number of distance calculations significantly. Compared to FPNN, our method can reduce the computing time by a factor of about 26.8 for the data set from a real image. Compared with PMLFPNN, our approach can reduce the computing time by a factor of about 3.8 for the same data set.
Relation: 43(12) pp.3958-3968
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50853
Appears in Collections:[資訊工程學系] 期刊論文

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