Pretreatment of Cotton Processing Data Based on SPSS

Abstract

In the processing of cotton, a large variety of data is generated, and researchers can use this data to conduct a large number of studies to improve the quality of cotton processing. Before mining the historical data, it is necessary to pre-process the dirty data of the actual application. According to the actual data provided by the cotton factory, the data is preprocessed by the raw data. By comparing the advantages and disadvantages of various algorithms, Regression filling method is used to process data missing values. The data is standardized by Z-score method, the data is processed into the same dimension, and the seed cotton data is clustered by K-means algorithm. We choose SPSS as the data preprocessing simulation software to provide effective high-quality data for the next step of data mining.