Intelligent Analysis and Processing of Big Data Based on Improved K-Means Clustering Algorithm

K-means: As one of the most widely used partitioning clustering algorithms, it is suitable for processing large-scale data sets. The algorithm optimizes the cluster centroid position by minimizing the sum of intra-cluster distances, which is suitable for processing continuous data. This paper explores the use of clustering algorithms, feature extraction and other technologies, and the use of improved clustering algorithms for intelligent analysis and processing of big data. This paper presents intelligent analysis and processing of big data based on improved K-Means clustering algorithm. In order to verify the effectiveness of the proposed clustering algorithm, this paper selects public large data sets and carries out comparative experiments. The experimental results show that the proposed clustering method can identify the cluster centers more accurately and improve the clustering accuracy effectively. The classification of big data is realized through the clustering algorithm model, which greatly improves the big data computing ability.