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.
Highlights
Social production and life have become increasingly prominent
In the data mining project, if a reasonable network model can not be determined in advance when dealing with some complex problems with the BP model, used for global optimal search, this algorithm is very effective for improving the accuracy and accuracy of data mining in CRM and obtaining a lot of valuable data
In order to improve the quality of clustering, people continue to explore and explore better clustering analysis methods. e group intelligence, self-adaptability, and robustness are shown by the group intelligence optimization algorithm; combined with the group intelligence optimization, cluster analysis develops rapidly
Summary
Social production and life have become increasingly prominent. Cluster analysis is the basis for further processing of the data. e concept of data mining and the application of neural networks in data mining are introduced. Cluster analysis is the basis for further processing of the data. E concept of data mining and the application of neural networks in data mining are introduced. The neural network-based data mining process consists of three stages: data preparation, rule extraction, and rule evaluation. 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. Eoretical analysis shows that the data mining clustering algorithm is very suitable for using neural computing. Since various methods have their own functional characteristics and applicable fields, the choice of data mining technology will affect the quality and effect of the final results. In the actual application process, multiple technologies are usually combined to form complementary advantages
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