DB-GNG: A constructive Self-Organizing Map based on densilty

Ocsa, Alexander; Bedregal, Carlos; Cuadros-Vargas, Ernesto

doi:10.1109/ijcnn.2007.4371257

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…In general, there are two main approaches to speed up the GNG algorithm. The first approach is to reduce the computational complexity of GNG, such as density-based growing neural gas (DB-GNG) [11] and the literature [12]. They usually establish a supporting structure, such as an R-tree, slim-tree or KD-tree, in order to reduce the cost of finding the nearest neuron.…”

Section: Related Workmentioning

confidence: 99%

An incremental self-organizing neural network based on enhanced competitive Hebbian learning

Líu

Kurihara

Oyama

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Self-organizing neural networks are important tools for realizing unsupervised learning. Recently, a difficult task has involved the incremental, efficient and robust learning in noisy environments. Most of the existing techniques are poor in this regard. In this paper, we first propose a new topology generating method called enhanced competitive Hebbian learning (enhanced CHL), and then propose a novel incremental self-organizing neural network based on the enhanced CHL method, called enhanced incremental growing neural gas (Hi-GNG). The experiments presented in this paper show that the Hi-GNG algorithm can automatically and efficiently generate a topological structure with a suitable number of neurons and that the proposed algorithm is robust to noisy data.

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Section: Related Workmentioning

confidence: 99%

An incremental self-organizing neural network based on enhanced competitive Hebbian learning

Líu

Kurihara

Oyama

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

show abstract

“…The purpose of our work is to provide a simultaneous two-level clustering approach using SOM, by learning at the same time the structure of the data and its segmentation, using both distance and density information. This new clustering algorithm assumes that a cluster is a dense region of objects surrounded by a region of low density (Yue et al, 2004;Ultsch, 2005;Ocsa et al, 2007;Pamudurthy et al, 2007). This approach is very effective when the clusters are irregular or intertwined, and when noise and outliers are present.…”

Section: Introductionmentioning

confidence: 99%