Characteristics Analysis of Applied Mathematics in Colleges and Universities Based on Big Data Mining Algorithm Model

Abstract

To analyze the mathematical nature of applied mathematics in colleges and universities, a method based on a model of big data mining algorithms is proposed. Firstly, the modeling is carried out through the deployment of nodes, which can accurately collect the characteristics of data information in the case of massive big data; secondly, the acquisition algorithm of multi-feature fusion is systematically optimized, which can avoid data interference and collect features quickly and accurately; thirdly, by transforming the multidimensional application-oriented university applied mathematics discipline model into an unlimited experience loss minimization problem with penalty factors, the improved support vector machine algorithm is used to construct and solve the objective kernel function. It is proved that the intelligent collection method based on big data mining algorithm model for the characteristics of applied mathematics in colleges and universities is effective. The sympathetic set corresponding to the flowing data is [115∼135]; the data similarity in big data environment is 1; in order to ensure that the intelligent collection method of applied mathematics discipline characteristics in colleges and universities based on big data analysis can collect data characteristics more accurately, which are 110/76.65/78/110, respectively.

Electronic Communication Fault Signal Recognition Based on Data Mining Algorithm

“…Cui and others proposed several evaluation methods to determine the relative significance of input variables in the data mining model. Several methods are applicable to classification tasks, and the practicability and accuracy are evaluated according to the characteristics of fault diagnosis [11]. Bai and others studied the ability of using data mining to predict faults and find anomalies early [12].…”