Generation of a clustering ensemble based on a gravitational self-organising map

Ilc, Nejc; Dobnikar, Andrej

doi:10.1016/j.neucom.2011.10.043

Cited by 18 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In GSOM, the basic idea is used and integrated with SOM, considering the connections between neurons (Ilc and Dobnikar, 2012). Rashedi et al (2009) have been proposed a stochastic populationbased metaheuristic, called Gravitational Search Algorithm (GSA), based on Newtonian law of gravity and the laws of motion.…”

Section: Related Workmentioning

confidence: 99%

“…the proposed gravity clustering is compared with nine methods including K-means, FCM, KFCM Chen, 2003a, 2003b), NJW (Ng et al, 2002), GKFCM (Gustafson and Kessel, 1979), PFCM (Pal et al, 2005), KPCA (Scholkopf et al, 1999), GSOM (Ilc and Dobnikar, 2012) and SGC (Teng and Jin, 2006). In the proposed method, the following settings are applied: p is set to 2 and the number of iterations, T, is set to 200 G is decreased linearly with time according to Eq.…”

Section: Comparative Algorithms and Parameters Settingmentioning

confidence: 99%

“…In these figures, the samples of two clusters provided by the clustering methods are presented by black triangle symbol "▽", and blue pentagram symbol " ". As mentioned above, the algorithms used in the competitions are K-means, FCM, KFCM Chen, 2003a, 2003b), NJW (Ng et al, 2002), GKFCM (Gustafson and Kessel, 1979), PFCM (Pal et al, 2005), KPCA (Scholkopf et al, 1999), GSOM (Ilc and Dobnikar, 2012), SGC (Teng and Jin, 2006) and the proposed gravity clustering.…”

Section: Visual Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

A data clustering approach based on universal gravity rule

Bahrololoum

Nezamabadi‐pour

Saryazdi

2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Section: Comparative Algorithms and Parameters Settingmentioning

confidence: 99%

Section: Visual Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

A data clustering approach based on universal gravity rule

Bahrololoum

Nezamabadi‐pour

Saryazdi

2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

“…Accordingly, existing approaches for color or feature-based color image segmentation (SGISA) [31] and the gravitational collapse (CTCGC) approach for color texture classification [36]. Other gravitation model based approaches for classification include data gravitation classification (DGC) [37][38][39] and gravitational self-organizing maps (GSOM) [40]. The advantages of the gravity field model have inspired the development of new edge-detection method for gray-scale images [41,42].…”

Section: Introductionmentioning

confidence: 99%

Gravitation-Based Edge Detection for Hyperspectral Images

Sun¹,

Zhang²,

Ren³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract:Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method.

show abstract

“…The contributions of this paper are: (i) the proposed two-layer random forests model can improve case reuse accuracy and stability; (ii) the gravitational self-organizing mapping (gSOM) algorithm (Ilc & Dobnikar, 2012) is adopted to organize the cases and improve the case retrieval efficiency. The two-layer model scheme contains two layers, where the first layer model pre-estimates the output and the second layer model is added to model the error of the first layer model.…”

Section: Introductionmentioning

confidence: 99%