Self-organizing map (SOM) is one of the most popular neural network methods for cluster analysis. Clustering methods using SOM usually are two-stage procedures: first original data are projected onto a set of prototypes on an ordered grid by SOM, and these prototypes can be seen as proto-clusters which will be grouped in the second stage to obtain finally clustering results. Many methods have b...

The self-organizing map (SOM) is a nonlinear unsupervised method for vector quantization. In the context of classification and data analysis, the SOM technique highlights the neighbourhood structure between clusters. The correspondence between this clustering and the input proximity is called the topology preservation. We present here a stochastic method based on bootstrapping in order to incre...

In this paper it is presented a framework environment for neural network simulation through the development of e-Services running on the EGEE Grid infrastructure. Parallel computing and distribution of jobs can be synchronized by the e-Service through a web interface. This interface provides an easy to use environment and can be requested over the internet. This simulation tool is based on a ne...

Network Intrusion Detection (NID) is the process of identifying network activity that can lead to the compromise of a security policy. In this paper, we will look at four intrusion detection approaches, which include ANN or Artificial Neural Network, SOM, Fuzzy Logic and SVM. ANN is one of the oldest systems that have been used for Intrusion Detection System (IDS), which presents supervised lea...

This paper introduces two clustering methods to be used in the analysis of network traffic. The methods are the self-organizing map (SOM) and the fuzzy c-means clustering (FCM) algorithm. First the basic theory of both methods is presented. Before the clustering process, a comprehensive data preprocessing is performed. Methods are used to produce application profiles and clusters from network t...

The paper presents a novel artificial neural network type, which is based on the learning rule of the Kohonen-type SOM model. The developed Self-Organizing Neuron Graph (SONG) has a flexible graph structure compared to the fixed SOM neuron grid and an appropriate training algorithm. The number and structure of the neurons express the preliminary human knowledge about the object to be detected, ...

This paper introduced about the data aggregation in wireless sensor network. We have presented SOM Based Data Aggregation Algorithm in Neural Network based Multihop Wireless Sensor Network and AdWAS: Adaptive Weighted Aggregation Scheme for Single-hop and Multi-hop Wireless Sensor Network. Among the distributed detection in neural network gives better result than the other technique when the tw...