Multi-Source Big Data Information Mining Method Based on Genetic Algorithm and Cloud Computing
Traditional data mining methods are often difficult to deal with multi-source big data,resulting in inaccurate and incomplete mining results.Therefore,through the in-depth research and analysis of multi-source big data information mining methods based on genetic algorithm and cloud computing,it aims to provide new theoretical support and practical guidance for the field of big data information mining,and promote the further development and application of big data technology.First,extract the characteristics of the multi-source big data information,ac-cording to the evaluation criteria,and then repeatedly screening until the best feature subset,sec-ondly,through the genetic algorithm of mining task fitness function design,big data informa-tion global search,to lay a solid foundation for big data mining,finally,based on cloud compu-ting to realize the big data information deep mining,provide strong support for data analysis and decision-making.Experimental results:Cloud computing platform and genetic algorithm were used to process and analyze the multi-source big data in parallel,which significantly improved the data processing speed.Compared with the traditional single-machine processing method,the data processing time in the cloud computing environment is greatly shortened,while maintai-ning a high processing quality.
multi-source big datacloud computinginformation mininggenetic algorithm
NETL
NSTL
万方数据
