Abstract: In the training process of the Self-Organizing Map,different classes of data have varying effects on the update of the weight matrix.Therefore,the update of the weight matrix for a certain class of data will have an impact on the feature vectors of the winning neurons,which are corresponding to other classes of data.This impact causes the winning neurons to deviate from the features of the data,thus reducing the clustering accuracy of the algorithm.Regarding the above issue,this paper proposes an improved confidence-based SOM model(icSOM).Firstly,the sample data is classified by the K-means algorithm to provide more information for model training.Secondly,the pre-classified data is used for training different classes SOM models to eliminate the influence caused by data from different classes.Based on the traditional SOM model,the concept of confidence matrix is then proposed.By comprehensively evaluating the confidence of the winning neurons and their Euclidean distance to the input data,the confident neuron is finally obtained.The clustering label that assigned to this input data is same as this confident neuron's class.Using icSOM for clustering analysis of the Iris dataset and the Wine dataset,the experimental results show that the proposed algorithm can handle sample data more effectively and achieve better clustering performance.

Key words: Machine learning, Unsupervised learning, Clustering, Self-organizing feature map neural network