Redefining Platform Data Mining Paradigms with Sequential Quadratic Programming Considering Optimization Models
Xiong Wenting · 2025
Platform data mining is an important branch of data analysis. Traditional methods such as clustering have achieved satisfactory performance. To overcome the shortcomings of traditional algorithms in mathematical optimization, this study proposes a novel model based on the sequential quadratic programming algorithm. At the algorithm level, this study first analyses the characteristics of cloud platform data and its requirements for mining efficiency. At the same time, to solve these problems, this study proposed a new data analysis framework that combines sparse factor analysis and embedded database subspace detection. The designed model optimizes attribute dimension selection and dense region extraction to the greatest extent through distribution analysis and feature correlation evaluation. At the same time, the Bayesian network node expansion algorithm is used to model the association of discrete data, and then a cascade data generation method is designed based on this model. Finally, the Bayesian network parameters are optimized by the sequential quadratic programming algorithm, and the approximate value of the Hessian matrix is efficiently solved by the BFGS algorithm, thereby improving the accuracy of the data mining algorithm. The experimental part uses the cloud platform data-set as the target data-set and verifies the stability of the proposed algorithm.