Analysis of Machine Learning Based Big Data Mining System for Enterprise Businesses

The primary objective of this paper is to conduct an in-depth analysis of a machine learning-based enterprise business big data mining system. Firstly, the introduction section presents the background, significance, and objectives of the research. Secondly, we provide an overview of the fundamental concepts of data mining and machine learning, including data mining methods and machine learning algorithms. Specifically, we discuss data mining methods such as the Apriori algorithm, clustering algorithm, and decision tree, as well as machine learning algorithms such as the Naive Bayes algorithm and K-nearest neighbors method. Furthermore, we introduce the principles and applications of the big data processing framework Hadoop. Subsequently, the design and implementation of the enterprise big data mining system are discussed. Finally, we conduct experiments on the enterprise big data mining system. We present a detailed description of the experimental process, including the introduction of experimental data, the experimental environment, and parameter settings, as well as specific experimental steps and procedures. Additionally, we showcase the experimental results and conduct evaluation and analysis. The research in this paper aims to provide valuable guidance and practical experience for enterprises in the field of business big data mining, promoting the intelligence and precision of decision-making, and enhancing the healthy development and competitive advantage of businesses.