Research on Big Data Mining Algorithms Based on Fast Spark Artificial Intelligence Architecture

This article primarily investigates big data mining algorithms based on the rapid Spark AI architecture. By analyzing the core technologies of the Spark framework and its applications in big data processing, it explores the implementation of various clustering algorithms on Spark, including partitioning-based, hierarchical, density-based, and grid-based clustering methods. In particular, this paper highlights a probabilistic parallel clustering algorithm—CK-means—and provides a detailed discussion of its parallelization implementation in a big data environment. Experimental results demonstrate that the CK-means algorithm significantly enhances computational efficiency and accuracy when handling large-scale data. By comparing the performance of different algorithms, this study offers an efficient solution for big data mining and contributes to the advancement of big data technologies in practical applications.