Intelligent Fuzzy Optimization Algorithm for Data Set Information Clustering Patterns Based on Data Mining and IoT - Heming Chen; Zheng, Chunrui (Wireless communications and mobile computing, 2022)
Social creation and life have become progressively unmistakable. Bunch examination is the reason for additional handling of the data. The ideas of data mining, as well as the use of neural networks in data mining, are examined. The two-layer perceptron, back engendering (BP) neural network, and RBF extended premise work network are totally depicted exhaustively in this article data mining, as demonstrated by the associated development of data mining and handling characterization issues, and self-sorting out map (SOM) is a self-assembling neural network for unaided grouping issues. As indicated by the qualities of self-versatile and self-arranging capacities, we learn, plan, and execute data mining grouping optimization techniques using these algorithms. This study isolates the neural network-based data mining technique into three phases: data course of action, rule extraction, and rule evaluation. This work centers around the teaching type and breaking down type rule extraction strategies. The connection approach is used to compute the association between the information and result neurons after researching the BP disintegration type method. After you have planned everything out, it is time to put it all together. The RBF neural network is utilized to pick center points in light of association levels. This can assist with diminishing the quantity of data centers in the neural network; further develop the network geography; decline the quantity of recursive segments in the subnet, in addition to other things; and further develop computation productivity. Accepting the model, for instance, the preparation mistake is determined through the use of data mining and a bunching algorithm. For the most part, the data mining grouping optimization method works on from two perspectives: better model planning and model pruning and rebuilding of the well-known neural network tests that duplicate model complexity, computational intricacy, and blunders. After that, the rate is calculated, and finally, the recreation investigation is carried out. The findings indicate that the proposed differential dispersed data mining algorithm is more exact and effective. More grounded combination capacity defeats the deficiencies and weaknesses of a few unique hereditary algorithms. It can truly work on the algorithm’s accessibility and search precision, as well as the usefulness of data mining employing neural network data mining models based on algorithm optimization. Precision and exactness are useful in a variety of situations. The rate is then computed, and ultimately, the reproduction study is conducted. The findings show that the proposed differential allocated data mining method works. The algorithm is more precise and more grounded in intermingling capacity and conquers the inadequacies and weaknesses of a few unique hereditary algorithms. It can successfully further improve the algorithm’s inquiry capacity and search precision, as well as work on the productivity of data mining, through algorithm optimization neural network data mining models. Precision and exactness have several applications.