An Improved SOM Algorithm And Its Application In Clustering

Clustering is an unsupervised learning process,its main purpose is to no markedcharacteristics of the data into the same group.Mainly divided into the method, hierarchicalmethods,density-based method,grid-based approach model-based approach.model-basedapproach,SOM(Self-Organizing Maps)neural network is a typical unsupervised clusteringalgorithm,which is 1981,made by the Finnish scholar Kohonen self-organizing featureneural network model,such as they have topology to keep,maintain a probabilitydistribution, unsupervised learning and visualization features,are widely used in many areasof information processing can be used for speech recognition,image compression, robotcontrol,optimization control theory,financial analysis,experimental physicsscience,chemistry,medicine.This paper studies the self-organizing neural network algorithm for both the currentshortcomings remain GSOM to generate new neurons generated by the network constraintsand neuron to advance to a threshold,and an improved dynamic SOM algorithm,and aFellow of various indicators of cell testing data automatically cluster classification,compared with the traditional SOM algorithm to verify the algorithm efficiency.Improvedalgorithm has the following main innovations 1) do not need to set the number of neuronsbefore the experiment,completely self-organizing unsupervised learning,automaticclustering;2) analysis of variance based on the growth of ideas,no rule of thumb,or othersuitable control the growth of computing factor SF; 3) pruning process,specifically excludeabnormal noise data;4) circular network structure,there is no problem can not be arrangedgenerated neurons can adapt to free growth;The algorithm matlab programming tools,toachieve a classification and comparison of the clustering process.