genetic based share market prediction
Harshada Ashok Kore,Samata Ajitkumar Gandhi,Gaikwad Neha Pandharinath
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 11 April,2015 Pages:397-406
Abstract
This paper proposes a system that will provide predictions about the share market, which will follow two main phases, which are fragment based association mining and further on more optimization and prediction will be provided by genetic algorithm. The major advantage of using fragment based mining is that, it groups all the attributes once and performs operations group wise instead of single attributes which results in more generalized rules which are further highly optimized using genetic algorithm as its time space complexity is less than any other algorithm and provide prediction of small scale companies based on transaction data of large scale as well as small scale companies.
Kewords
Data mining, Fragment based mining, Association rule mining, optimization, Genetic algorithm
Reference
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