Title: Association rule mining using enhanced apriori with modified GA for stock prediction

Authors: S. Prasanna; D. Ezhilmaran

Addresses: School of Information Technology, VIT University, Vellore, India ' School of Advanced Sciences, VIT University, Vellore, India

Abstract: In stock marketing, picking the right stock depends on the true stock value and the ability to pick the stock is crucial as it influences the profit of investors. Data mining techniques have been used for forecasting the stock market price and have shown successful results too. Yet, the investors are looking for a genuine forecasting model to predict the stock rule more efficiently. This work intends to form a technique based on association rule mining using enhanced apriori algorithm with modified genetic algorithm for the estimation of fine stock rule. The enhanced apriori algorithm mainly focuses on association rule mining and hence to avoid the time computation complexity. This modified GA uses interrelating crossover and mutation operations. These genetic operations avoid genetic algorithm from premature convergence and hence, enables strong association rules to be generated.

Keywords: data mining; apriori algorithm; stock rules; genetic algorithms; association rules mining; stock prediction; stock markets; stock prices; forecasting models.

DOI: 10.1504/IJDMMM.2016.077162

International Journal of Data Mining, Modelling and Management, 2016 Vol.8 No.2, pp.195 - 207

Received: 27 May 2014
Accepted: 04 Oct 2014

Published online: 21 Jun 2016 *

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