基于FP-树的多元股票时间序列跨事务关联挖掘

Mining association rules from transactions occurred at different time series is a difficult task because of high computational complexity, very large database size and multidimensional attributes. Traditional techniques, such as fundamental and technical analysis can provide investors with tools for predicting stock prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. We propose a framework called InterTARM on real datasets. Our approach employs effective preprocessing, pruning techniques and available condensed data structure to efficiently discover inter-transaction association rules.