Research on Optimizing Data Preprocessing Using Clustering Algorithms

This paper focuses on the application of clustering technology in data preprocessing to enhance the comprehensive leap of data analysis efficiency and accuracy. This experiment introduces the K-means clustering algorithm, standardizes the wholesale customer data set, and adopts the principal component analysis dimensionality reduction method to implement clustering process improvement. The experimental results show that, relying on the detailed cluster analysis method, the data resources are successfully subdivided into three major customer categories, the consistency within the data group is significantly enhanced, and the recognition between clusters is greatly improved. The research results show that clustering technology has significantly innovated the means of data preprocessing and effectively supported the subsequent data mining and decision-making implementation. We will continue to deepen the research, explore ways to integrate multiple machine learning strategies, and discuss technical paths to deal with complex data environments.