A Generalisation Of The Fuzzy C-means Clustering Algorithm

Tilson, L.V.; Excell, Peter S.; Green, R.J.

doi:10.1109/igarss.1988.569600

Cited by 23 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such the training time can be reduced and the overfitting effects minimized. Fuzzy C-means clustering (FCM) (Tilson et al, 1988) is a widely used clustering method, which avoids the deficit of the sub-clusters with unequivocal similarities within its components (Mukerji et al, 2009). In FCM, every single event is given a membership u, which indicates the relation between the event and a certain cluster.…”

Section: Fuzzy C-means Clustering and Principal Component Analysismentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Lin

Leandro

et al. 2020

Front. Earth Sci.

View full text Add to dashboard Cite

In many countries, floods are the leading natural disaster in terms of damage and losses per year. Early prediction of such events can help prevent some of those losses. Artificial neural networks (ANN) show a strong ability to deal quickly with large amounts of measured data. In this work, we develop an ANN for outputting flood inundation maps based on multiple discharge inputs with a high grid resolution (4 m × 4 m). After testing different neural network training algorithms and network structures, we found resilience backpropagation to perform best. Furthermore, by introducing clustering for preprocessing discharge curves before training, the quality of the prediction could be improved. Synthetic flood events are used for the training and validation of the ANN. Historical events were additionally used for further validation with real data. The results show that the developed ANN is capable of predicting the maximum flood inundation extents. The mean squared error in more than 98 and 86% of the total area is smaller than 0.2 m 2 in the prediction of synthetic events and historical events, respectively.

show abstract

Section: Fuzzy C-means Clustering and Principal Component Analysismentioning

confidence: 99%

“…Here different results from clustering different sets of parameters obtained from the hydrographs are evaluated using the index L(c) (Tilson et al, 1988). Larger numbers indicate that the selected parameters are more suitable.…”

Section: Fuzzy C-means (Fcm) Clusteringmentioning

confidence: 99%

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Lin

Leandro

et al. 2020

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…At the end of the process, each pixel is assigned the estimated spectral signature and spatial location of the local mode of the probability density function it belongs to. Finally, the objects were automatically classified into two classes using the FCM clustering method [62], [63]. The landslide objects were labelled using the visual interpretation of LiDAR imageries.…”

Section: F Landslide Detection With Morphological and Fcm Clusteringmentioning

confidence: 99%

Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos

et al. 2020

View full text Add to dashboard Cite

This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments.

show abstract

“…or the wider class of Bregman divergences (Bregman, 1967;Banerjee et al, 2005;Singh and Gordon, 2008). In addition to the soft constraints imposed by the penalty terms W (W) and Z (Z), the feasible regions Z ⊂ R T ×k and W ⊂ R d×k define a set of hard constraints that must be obeyed by the optimal solutions.…”

Section: Matrix Factorizationsmentioning

confidence: 99%

Applications of matrix factorization methods to climate data

Harries

O’Kane

2020

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. An initial dimension reduction forms an integral part of many analyses in climate science. Different methods yield low-dimensional representations that are based on differing aspects of the data. Depending on the features of the data that are relevant for a given study, certain methods may be more suitable than others, for instance yielding bases that can be more easily identified with physically meaningful modes. To illustrate the distinction between particular methods and identify circumstances in which a given method might be preferred, in this paper we present a set of case studies comparing the results obtained using the traditional approaches of empirical orthogonal function analysis and k-means clustering with the more recently introduced methods such as archetypal analysis and convex coding. For data such as global sea surface temperature anomalies, in which there is a clear, dominant mode of variability, all of the methods considered yield rather similar bases with which to represent the data while differing in reconstruction accuracy for a given basis size. However, in the absence of such a clear scale separation, as in the case of daily geopotential height anomalies, the extracted bases differ much more significantly between the methods. We highlight the importance in such cases of carefully considering the relevant features of interest and of choosing the method that best targets precisely those features so as to obtain more easily interpretable results.

show abstract

A Generalisation Of The Fuzzy C-means Clustering Algorithm

Cited by 23 publications

References 3 publications

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos

Applications of matrix factorization methods to climate data

Contact Info

Product

Resources

About