Intelligent data fast retrieval algorithm analysis based on data mining and machine learning
With the advent of the era of big data,how to quickly retrieve massive data is a difficult problem.This research in-tegrates data mining and machine learning algorithms,aiming to solve the difficult problem of rapid retrieval of massive data in the era of big data,improve the efficiency and accuracy of retrieval,and provide strong support for big data processing and analysis.First of all,this study improved the decision tree algorithm that integrates data mining and machine learning,then constructed the optimized model and conducted practical application analysis,and then compared the performance of the improved decision tree algorithm.The results show that when the number of information retrieval conditions is 6,the accuracy rate and recall rate of the improved decision tree algorithm are the highest and both are 93％.Finally,the empirical analysis of the intelligent data fast retrieval model of the im-proved decision tree algorithm shows that the retrieval time of the proposed model in this study is the fastest among 0-300 files,which is 10ms.It can be seen that the improved decision tree algorithm can achieve high performance data mining and retrieval functions,and provide strong support for the application of related fields.
data miningmachine learningfast data retrievalfeature selection
NETL
NSTL
万方数据
