A Highly Efficient Big Data Mining Algorithm Based on Stock Market

This article proposes a new algorithm which includes two stages. First, the Pearson correlation coefficientisusedtocalculatethesimilaritydata,andtheactivityofstockmoneyflowwascalculated bycombinedtheprobabilitygeneratingfunction(P.G.F.)ofstationarywaitingtimeandstationary queuelength.Second,thediscretetimeGeo/G/1queuewithaBernoulligatedserviceisproposed incalculatingmoneyflowbydataminingofstock.Thenewalgorithmcouldcalculatedatainreal time, and each investor could see the real-time data mining graphics. Investors could establish theirquantitativetradingstrategiesbasedonthenewmoneyflowmodel.Theproposedalgorithm exploitsthenaturebehindstockdata.Theexperimentalresultsshowthattheauthors’approachcan beautomatically implementedby the investmentstrategyandknowthefuture trendof thestock market,aswellastheeconomicdevelopmentoftheregion,accordingtotheresultsofthestockdata mininginacertainregion. KEywoRDS Big Data, Data Mining, Eastern Region, Geo/G/1 Queue, Money Flow, Western Region

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