Clustering Optimization Algorithm for Data Mining Based on Artificial Intelligence Neural Network

Abstract

Social production and life have become increasingly prominent. Cluster analysis is the basis for further processing of the data. The concept of data mining and the application of neural networks in data mining are introduced. According to the related technology of data mining, this article introduces in detail the two-layer perceptron, backpropagation (BP) neural network, RBF radial basis function network for processing classification problems, and self-organizing map (SOM) self-organizing neural network for unsupervised clustering problems. According to the characteristics of self-adaptive and self-organizing capabilities of these algorithms, we learn and design and implement data mining clustering optimization algorithms. In this paper, the neural network-based data mining process consists of three stages: data preparation, rule extraction, and rule evaluation. This paper studies the teaching-type and decomposition-type rule extraction algorithms. After analyzing the BP decomposition-type algorithm, the correlation method is used to calculate the correlation of the input and output neurons. After sorting by the degree of correlation, the RBF neural network is used for node selection. This can greatly reduce the number of input nodes of the neural network, simplify the network structure, reduce the number of recursive splits of the subnet, and improve calculation efficiency. Taking the model as an example, the training error is calculated through data mining technology and clustering algorithm. Data mining clustering optimization algorithm mainly improves the popular neural network from two aspects: finer model design and model pruning, and simulates model complexity, computational complexity, and errors through simulation experiments. The rate is measured, and finally, the simulation experiment is performed. The results show that the proposed algorithm for differential distributed data mining has higher accuracy and stronger convergence ability and overcomes the shortcomings and shortcomings of several original genetic algorithm optimization neural network data mining models; it can effectively improve the searchability and search accuracy of the algorithm and improve the efficiency of data mining. Accuracy and accuracy have a wide range of applications.