EFFICIENT MINING OF FAST FREQUENT ITEM SET DISCOVERY FROM STOCK MARKET DATA

Stock market is a changeable environment. Traditional data analysis techniques using of some tools can provide investors to manage stocks and predict prices. However these traditional techniques cannot determine all the possible relations between stocks and that’s why needing a different approach that will provide deeper kind of analysis. Data mining can be use comprehensively in the stock-price predicting. In this paper investigators propose a new approach with efficient preprocessing, pruning data structure techniques to discover inter-transaction association rules with business intelligence characteristics. Propose work also provides better in-depth study of intertransaction stock price movement of companies to financial research community, money managers, fund managers, investors, etc.