The Parallelism And Application In Data Mining Of BP Algorithm

Data mining technology is used to help people finding the information and knowledge in the data. It has become the core technology of the intelligence commerce. It has been widely used in many areas and drawn the attention of the whole academe. Some algorithms and techniques of artificial intelligence, including neural networks, have been applied in data mining to do prediction, pattern recognition, classification and Clustering. One important application of neural network in data mining is sales trend prediction. BP (Back Propagation) algorithm is the most popular training algorithm in applications for its non-linear mapping approach capability and robustness. However, it is known to have some defects, such as converging slowly and immersing in local vibration frequently. Generally, we often choose small random initial weights to void training process immerse in local minimum. If it is far from chosen range of weights to goal area, the search space is wider, goal area is narrower, search time is longer and training speed is slower. To solve this problem, the paper proposed a solution named two times parallel search strategy, that is, obtaining global minimum area by dividing weight space unequally at first and then training network using data parallelism. The experiment results show that the strategy reaches global minimum soon and converges at high rate, especially to a large training samples. The hardware platform is PC connected with LAN. The software platform is PVM and LINUX. They construct the whole PC-cluster system. The parallel program model is master/slave model. The algorithm assign data set to each node realizes the data-parallel. The application of BP algorithm in data mining is discussed in this paper. The strategy mentioned is applied to sales prediction of medicine logistics system and a sales prediction model based on parallel algorithm is established. How to choose and preprocess training set and how to select network topology is proposed in detail in this paper. At last, a visual prediction system is realized to achieve prediction result, which makes prediction works easy.