An Edge based Clustering Technique with Self-Organizing Maps

Chamundeswari, G.; Varma, Girish; Satyanarayana, Ch.

doi:10.5815/ijitcs.2018.05.03

Cited by 2 publications

(1 citation statement)

References 21 publications

(18 reference statements)

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“…In previous studies on the SOM clustering technique application, the distance from a particular neuron to its neighbors has often been calculated using Euclidean [34,37,38] and link functions [39,40]. Comparisons of SOM results using the four distance functions [38,41,42] show that there is no "golden rule" to select a best distance function because each of the three functions, i.e., Euclidean, Manhattan, and link distance, is able to work better than others in different experiments. According to [36,43], the Euclidean and link distance functions are two of the most common for SOM analysis.…”

Section: Self-organizing Map Analysismentioning

confidence: 99%

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

et al. 2020

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The spatial and temporal variability of the Kuroshio surface axis northeast of Taiwan Island is investigated using 24 years of surface geostrophic currents derived from satellite altimeter data from 1993 to 2016. The Kuroshio surface axis is derived by an extraction method with three selected parameters, including the length of the subsidiary line, the intervals between two adjacent points, and the distance between the two adjacent subsidiary lines. The empirical mode decomposition analysis on the 24-year Kuroshio axes reveals that the mean periods of intra-seasonal and inter-annual variability, which are the two dominant components, are about 3.2 months and 1.3 years, respectively. The self-organizing map analysis reveals that the variation of Kuroshio axis northeast of Taiwan Island has four best matching unit (BMU) patterns: straight-path (BMUS), meandering-path (BMUM) and two transition stages (BMUT1 and BMUT2). The straight-path pattern shows strong seasonality: more likely occurring in summer. The meandering-path pattern is less frequent than straight-path pattern. During a typical period from November 26, 2012 to January 27, 2013, which is chosen as an independent example, the analysis on the satellite altimeter and sea surface temperature data shows that the patterns of the Kuroshio axis change successively in order of BMUT1→BMUM→BMUT2→BMUS, i.e., the Kuroshio axis migrates from the meandering-path to the straight-path pattern. During the typical period the warm water intrusion and a mesoscale eddy occur at the second stage corresponding to BMUM and migrate northwestward gradually at the last two stages corresponding to BMUT2 and BMUS. The transient order appears only during this typical period but it is not common for the whole study period. The monthly mean relatively vorticity is calculated and analyzed to evaluate the impact of the eddies on the Kuroshio surface axis variability, the results show that the anticyclonic (cyclonic) eddies can promote the Kuroshio surface axis to present the meandering-path (straight-path) pattern because of the potential vorticity conservation. The impacts of the anticyclonic eddies and the cyclonic eddies on the variability of the Kuroshio surface axis are opposite. The long-term day-to-day detection contributes to improving understanding the variability of Kuroshio surface axis northeast of Taiwan Island.

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Section: Self-organizing Map Analysismentioning

confidence: 99%

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

et al. 2020

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Unsupervised Learning Approach for Clustering Leaf Images

Chamundeswari

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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With appropriate representation methods, the clustering techniques are found to be efficient with neural networks. The present work aims to propose various feature representation techniques for efficient clustering. The methods used for feature representation in this paper are, a method using random closed set, a method using edge information of input entity, a method that uses Huff transformation and a method that uses boundary moments. A comparative study of these representation methods for clustering the input objects using artificial neural networks, specifically Self-Organizing Map (SOM) is focused.

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An Edge based Clustering Technique with Self-Organizing Maps

Cited by 2 publications

References 21 publications

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

Unsupervised Learning Approach for Clustering Leaf Images

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