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Self Organizing Map (SOM)

Self Organizing Map(SOM) by Teuvo Kohonen provides a data visualization technique which helps to understand high dimensional data by reducing the dimensions of data to a map. SOM also represents clustering concept by grouping similar data together. Therefore it can be said that SOM reduces data dimensions and displays similarities among data. With SOM, clustering is performed by having several units compete for the current object. Once the data have been entered into the system, the network of artificial neurons is trained by providing information about inputs. The weight vector of the unit is closest to the current object becomes the winning or active unit. During the training stage, the values for the input variables are gradually adjusted in an attempt to preserve neighborhood relationships that exist within the input data set. As it gets closer to the input object, the weights of the winning unit are adjusted as well as its neighbors. Self-Organizing Maps | Jae-Wook Ahn and Sue Yeon Syn

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Self-Organizing Networks (SON)

Self-Organizing Networks (SON) significantly improve performance by automatically identifying and fixing problems. Future wireless cellular network is highly expected to comprise of a huge number of small cells and heterogeneous networks. In this context, self-organization is emerging as a promising solution towards truly ubiquitous and broadband wireless services. This tutorial provides an overview on existing literature, research projects, and standards in self-organizing cellular networks. It is also aimed to present a clear understanding of this active research area identifying a clear taxonomy and guidelines prevalent for the design of self-organizing algorithms. We compare strength and weakness of existing solutions and highlight the open research areas. Furthermore, in the context of small cells and heterogeneous networks, new research challenges are identified and open research areas in a self-organizing manner are investigated from energy perspective.