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Title:
A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation
Author: Chang, DX ; Zhao, Y ; Liu, L ; Zheng, CW
Keyword: Clustering ; Genetic algorithms ; Niching ; Connected individual ; k-distance neighborhood ; Image segmentation
Source: PATTERN RECOGNITION
Issued Date: 2016
Volume: 60, Pages:334-347
Indexed Type: SCI
Department: Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China;Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China. [Chang, Dongxia; Zhao, Yao; Liu, Lian] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China. [Zheng, Changwen] Chinese Acad Sci, Inst Software, Natl Key Lab Integrated Informat Syst Technol, Beijing 100080, Peoples R China.
Abstract: In this paper, a dynamic niching clustering algorithm based on individual-connectedness (DNIC) is proposed for unsupervised classification with no prior knowledge. It aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k-distance neighborhood algorithm. More specifically, with the adaptive selection of the number of the nearest neighbor and the individual-connectedness algorithm, DNIC often achieves several sets of connecting individuals and each set composes an independent niche. In practice, each set of connecting individuals corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. An application of the DNIC clustering algorithm in color image segmentation is also provided. Experimental results demonstrate that the DNIC clustering algorithm has high performance and flexibility. (C) 2016 Elsevier Ltd. All rights reserved.
English Abstract: In this paper, a dynamic niching clustering algorithm based on individual-connectedness (DNIC) is proposed for unsupervised classification with no prior knowledge. It aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k-distance neighborhood algorithm. More specifically, with the adaptive selection of the number of the nearest neighbor and the individual-connectedness algorithm, DNIC often achieves several sets of connecting individuals and each set composes an independent niche. In practice, each set of connecting individuals corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. An application of the DNIC clustering algorithm in color image segmentation is also provided. Experimental results demonstrate that the DNIC clustering algorithm has high performance and flexibility. (C) 2016 Elsevier Ltd. All rights reserved.
Language: 英语
WOS ID: WOS:000383525600028
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/17290
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Chang, DX,Zhao, Y,Liu, L,et al. A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation[J]. PATTERN RECOGNITION,2016-01-01,60:334-347.
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