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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50851

Title: An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list
Authors: Jim Z.C. Lai
Tsung-Jen Huang
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Nearest neighbor
Agglomerative clustering
Vector quantization
Date: 2011-05
Issue Date: 2018-10-29T02:00:47Z
Publisher: Information Sciences
Abstract: Abstract: In this paper, a new algorithm is developed to reduce the computational complexity of
Ward’s method. The proposed approach uses a dynamic k-nearest-neighbor list to avoid
the determination of a cluster’s nearest neighbor at some steps of the cluster merge. Double
linked algorithm (DLA) can significantly reduce the computing time of the fast pairwise
nearest neighbor (FPNN) algorithm by obtaining an approximate solution of hierarchical
agglomerative clustering. In this paper, we propose a method to resolve the problem of a
non-optimal solution for DLA while keeping the corresponding advantage of low computational
complexity. The computational complexity of the proposed method DKNNA + FS
(dynamic k-nearest-neighbor algorithm with a fast search) in terms of the number of distance
calculations is O(N2
), where N is the number of data points. Compared to FPNN with a
fast search (FPNN + FS), the proposed method using the same fast search algorithm
(DKNNA + FS) can reduce the computing time by a factor of 1.90–2.18 for the data set from
a real image. In comparison with FPNN + FS, DKNNA + FS can reduce the computing time by
a factor of 1.92–2.02 using the data set generated from three images. Compared to DLA
with a fast search (DLA + FS), DKNNA + FS can decrease the average mean square error
by 1.26% for the same data set.
Relation: 181 pp.1722-1734
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50851
Appears in Collections:[資訊工程學系] 期刊論文

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