Topology-Based Hierarchical Clustering of Self-Organizing Maps

Taşdemir, Kadim; Milenov, P; Tapsall, Brooke

doi:10.1109/tnn.2011.2107527

Cited by 58 publications

(40 citation statements)

References 29 publications

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“…where a i is the asymmetric coefficient defined in Subsection 2.1, in (1), and the rest of notation is described in (5). The asymmetric similarity defined in this way using the asymmetric coefficient, guarantees the consistency with the asymmetric hierarchical associations among objects in the dataset.…”

Section: Asymmetric Self-organizing Mapmentioning

confidence: 99%

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Asymmetric k-means Clustering of the Asymmetric Self-Organizing Map

Olszewski

Kacprzyk

Zadrożny

2014

Artificial Intelligence and Soft Computing

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Abstract. In this paper, an asymmetric approach to clustering of the asymmetric Self-Organizing Map (SOM) is proposed. The clustering is performed using an improved asymmetric version of the well-known kmeans algorithm. The improved asymmetric k-means algorithm is the second proposal of this paper. As a result, we obtain the two-stage fullyasymmetric data analysis technique. In this way, we maintain the structural consistency of the both utilized methods, because they are both formulated in asymmetric version, and consequently, they both properly adjust to asymmetric relationships in analyzed data. The results of our experiments confirm the effectiveness of the proposed approach.

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Section: Asymmetric Self-organizing Mapmentioning

confidence: 99%

“…The approach, proposed in this paper, belongs to such a class of data analysis methods. The SOM clustering, itself, has been extensively studied, and a variety of solutions has been developed [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric k-means Clustering of the Asymmetric Self-Organizing Map

Olszewski

Kacprzyk

Zadrożny

2014

Artificial Intelligence and Soft Computing

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“…Then, these features were clustered by an unsupervised hierarchical method (CONN linkage [12) based on self organizing maps (SOM) and labeled by domain expert. We briefly explain the Gabor features and CONN linkage below.…”

Section: A Neural Network Based Unsupervised Clusteringmentioning

confidence: 99%

Unsupervised classification of vineyard parcels using SPOT5 images by utilizing spectral and textural features

Şentürk

Taşdemir

Kaya

et al. 2013

2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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In order to support agricultural management of vineyards, high spatial resolution remote sensing images (less than 1 meter) enables textural representation of their periodic plantation pattern which helps for delineation. Even though this texture analysis may provide highly accurate delineation of vineyards, it may be infeasible at national scale, due to the computational complexity of texture extraction. In addition, particularly for Turkey, plantation practices for vineyards deviate from common periodic pattern, which can make those textures insufficient. In this study, we used SPOTS images to explore their capabilities for delineation of vineyard parcels, without any a priori parcel information. As the inter-row distance and the spacing between the individual vine plants are less than the used 2.5m panchromatic, which is generated from 2x5m scenes (nadir) for panchromatic and 10m (nadir) spatial resolutions for multi-spectral bands, currently used periodicity based (Fourier) texture analysis may be vague. Therefore, we used Gabor textures (with different scales and directions) to define texture characteristics at this relatively coarse resolution, and we integrated these textures with image bands (visible, near infrared and shortwave infrared) which hold the ability to spectrally distinguish the vine plants from the remaining crops.For the vineyards parcels recognition, we classified the extracted features by a recent hierarchical clustering method based on self organizing neural networks. We compared the performance of this proposed method to the object-based image analysis (by eCognition) which depends on multi-scale image segmentation and user-defined decision rules with corresponding thresholds.

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“…This underutilizes the knowledge available at the output layer of the SOM given by data topology and data distribution Tasdemir Hence, to deal with fully unsupervised classification using SOMs, supervised clustering techniques have to be used at the output layer. In this sense, the CONN clustering method Tasdemir et al (2011), which is based on SOM visualization of a weighted Delaunay graph, not only performs unsupervised clustering but also takes into account data distribution and topology.…”

Section: Som Clustering With K-meansmentioning

confidence: 99%