A Data Dimensionality Reduction Algorithm for Aerospace Telemetry Data Mining

Data mining is an important field of intelligent computing and has been applied in many industries. The large amount of telemetry data accumulated by China's aerospace industry over the years is a warehouse that needs to be excavated. In order to solve the problems of high time complexity and rule space explosion in data mining of aerospace telemetry data, author proposes a data dimensionality reduction algorithm. By reducing the dimensionality of attribute space and record space, the number of calculation iterations is reduced, a large number of redundant and invalid attributes are eliminated, and discretize floating number, Finally, the effectiveness of the algorithm was verified through an example.