The Application Of The Deterministic Annealing Technique In Data Mining

Data mining technology is drawing the increasing concern of information field in recent years. The main reason is that there are abundant data, which are very important in researching. They need to be changed into the useful information and knowledge. Main model applications of data mining are classification, clustering, prediction, estimation and so on. This paper mainly aims at the research of classification, clustering and prediction. The ability of data mining lies on the efficiency of mining instrument. At present, there are three kinds of methods of solving learning questions: fuzzy rule, neural networks and genetic algorithm. But neural networks have particular advantages, such as nonlinear mapping and error correcting capability. Relative to other algorithms it has a further prospect. But it isn't ideal because it is fallible in solving down-to-earth questions. For example, it is easy for the system to fall into local optimization state when learning algorithm is unsuitable. Furthermore, there is a conflict between the complexity of network and generalization ability. Thus, the deterministic annealing technique of a new branch of physical computation, which can get over above-mentioned limitation, is applied to data mining.According to annealing process, the deterministic annealing technique transforms the optimum point to optimal solution into a series of the minimum of free energy function of a physical system, which varies with temperature. The deterministic annealing can make the algorithm avoid local minima and get global minima. The algorithm makes use of maximum entropy of information theory and gets optimal solution by a small-scale. On the one hand, the deterministic annealing technique is an annealing process and can be used to attain globally optimal solution. On the other hand, it is different from simulated annealing algorithm, which simulates the equilibrium state of system by use of sample rule in Metropolis. It simulates the equilibrium state of system by use of deterministic optimization methods to find the minimum of free energy function in a given temperature. It can be proved in theory that the global optimal solution is a continuous map to temperature when free energy function satisfies certain appropriate conditions. So the velocity of the deterministic annealing algorithm is faster than the simulated annealing algorithm. But the choosing of free energy function in the deterministic annealing varies with physical systems. It leads the difficulty in the giving of general searching measure for all free energy functions.In this paper, the deterministic annealing technique and RBF neural networks are combined in term of their advantages. The learning velocity and calculating velocity of RBF neural networks is superior to others. The deterministic annealing is applied to cluster and optimize the center of RBF neural networks parameter and networks width. It can remedy the problems of the sensitivity to initial value. At the same time, it can remedy the dead-node by probability. This not only ensures the advantages of every algorithm but also has new advantages by improving them. Simulation results show the validity of the method. It can be concluded from a variety of simulation results that the uniting of the deterministic annealing technique and RBF neural networks is very practicable, especially in large-scale data processing. As such the forecasting result is accurate in forecasting model.