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Rule Based Mining of Nifty Fifty Stock Market Data Prediction Based on Rough Set Theory

机译:基于粗糙集理论的五十种股票市场数据预测的规则挖掘

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Monetary gauging or uniquely securities exchange expectation is one of the most blazing field of examination of late because of its profitmaking applications inferable from high stakes and the sorts of alluring advantages that it brings to the table. This paper introduces rough sets creating forecast guidelines plan for stock value development. The plan had the capacity separate information as principles from every day stock developments. These tenets formerly could be utilized to guide financial specialists whether to purchase, offer or hold a stock. Toward expand the effectiveness of the forecast procedure, rough sets with Boolean thinking discretization calculation is utilized to discretize the information. Rough set decrease method is connected to ?nd every one of the reducts of the information. At long last, rough sets reliance guidelines are created specifically from every produced reduct. Harsh perplexity grid is utilized to assess the execution of the anticipated reducts and classes. The consequences of rough sets utilizing reducts structure by disarray network in choice table show general higher exactness rates of Decision making coming to more than 97% and create more minimized principle.
机译:货币计量或独特的证券交易期望是最近研究中最炽烈的领域之一,因为它可以从高额赌注中推断出获利的应用程序,并且可以带来各种诱人的优势。本文介绍了为股票价值开发创建预测准则计划的粗糙集。该计划具有将每天的股票开发信息作为原则分开的能力。这些原则以前可以用来指导金融专家是购买,提供还是持有股票。为了扩大预测程序的有效性,利用带有布尔思想离散化计算的粗糙集来离散化信息。粗集减少法被连接到信息的每一种归约。最后,将根据每个生产的约简创建粗糙集依赖准则。严峻的困惑网格被用于评估预期的还原和分类的执行。粗集在选择表中利用乱序网络利用约简结构的结果表明,决策制定的总体正确率更高,达到97%以上,并创造了最小化的原则。

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