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| A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation | |
| Chang, DX; Zhao, Y; Liu, L; Zheng, CW | |
| 2016 | |
| Source | PATTERN RECOGNITION
 |
| ISSN | 0031-3203 |
| Volume | 60Pages:334-347 |
| 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.; 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. |
| Indexed Type | SCI |
| Keyword | Clustering Genetic Algorithms Niching Connected Individual K-distance Neighborhood Image Segmentation |
| 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. |
| Language | 英语 |
| WOS ID | WOS:000383525600028 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/17290 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | 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,2016,60:334-347. |
| APA | Chang, DX,Zhao, Y,Liu, L,&Zheng, CW.(2016).A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation.PATTERN RECOGNITION,60,334-347. |
| MLA | Chang, DX,et al."A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation".PATTERN RECOGNITION 60(2016):334-347. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| A dynamic niching cl（1652KB） | 开放获取 | License | Application Full Text | |||
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