Data mining using genetic algorithm in finance data / A. Noor Latiffah and A. B. Nordin

Computing systems has enabled us to collect tremendous amount of data and information. A large pool of data requires not only an efficient and effective retrieval system but also a better way to discover hidden knowledge. Data mining can discover patterns or rules from a vast volume of data. This patterns or rules may help to develop better decision-making process. Data mining is primarily used in finance and business environment to extract knowledge from financial, retail, communication and marketing data. This project, wilt extract some useful financial knowledge from the Syariah Index data of Kuala Lumpur Syariah Index (KLSI). The methods that wilt be applied are conventional statistical methods Markowitz Optimization as well as evolutionary programming (EP) utilizing genetic algorithms. The result of this project are expected to be a comparison of the used methods that will give an indication how well evolutionary programming can perform relative to conventional method and how good the results of the data mining process.