Usage of Self-Organizing Map for Clustering Vertices
نویسندگان
چکیده
منابع مشابه
Clustering of Self-Organizing Map
In this paper, we present a new similarity measure for a clustering self-organizing map which will be reached using a new approach of hierarchical clustering. (1) The similarity measure is composed from two terms: weighted Ward distance and Euclidean distance weighted by neighbourhood function. (2) An algorithm inspired from artificial ants named AntTree will be used to cluster a self-organizin...
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Data clustering is the fundamental data analysis method, widely used for solving problems in the field of machine learning. Numerous clustering algorithms exist, based on various theories and approaches, one of them being the well-known Kohonen’s self-organizing map (SOM). Unfortunately, after training the SOM there is no explicitly obtained information about clusters in the underlying data, so...
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The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In t...
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The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
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We present a neural unsupervised pattern recognition approach for two applications related to significant topics of Earth Observation (EO) imagery: (a) EO image region classification; (b) multispectral pixel classification. The proposed model is based on the Self-Organizing Map (SOM) clustering, which is compared to two benchmark unsupervised classifiers: k-means and fuzzy c-means. We propose t...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications Technology and Research
سال: 2016
ISSN: 2319-8656
DOI: 10.7753/ijcatr0504.1005