A novel data mining classification method based on neural network

A neural network consists of units, arranged in layers, which convert an input vector into some output. Each unit takes an input, applies a function to it and then passes the output on to the next layer. Generally the networks are defined to be feed-forward: a unit feeds its output to all the units on the next layer, but there is no feedback to the previous layer. Weightings are applied to the signals passing from one unit to another, and it is these weightings which are tuned in the training phase to adapt a neural network to the particular problem at hand. This is the learning phase. Neural networks have found application in a wide variety of problems. These range from function representation to pattern recognition. This article explains the realization of BP Neural Networks and the application in data classification. Combining the features of the high tolerance of BP network to noisy data as well as the ability to classify the pattern not been trained, this paper discusses how to apply the BP network to realize classification in data mining. It presents a classification model of neural network to measure the performance of service quality for a system and service certification provider. The result shows that backpropagation neural network model is superior to linear discriminated analysis model. Copyright © 2010 Binary Information Press.