Application of self-organizing Feature Map Neural Network based on data clustering

Abstract

Outlier detection is of much importance in preprocessing of data collected from complex industry system, for the data has strong nonlinearity and poor stability, involving much noise. Outlier detection based on clustering, rejects abnormal data points which have significant difference from others according to the definition of similarity. Self-organizing Feature Map (SOM) Neural Network algorithm has the self-study and adaptive functions of neural networks, so as to be a hot research in clustering analysis recently. This paper first introduces Self-organizing Feature Map algorithm based on artificial neural network, and then improves the algorithm by using weighted Euclidean distance, finally uses the software of MATLAB to analyze some actual data of electrical power. The result shows that SOM algorithm achieves a very good effect in clustering, and the MATLAB toolbox shows favorable visual effects.