A Novel Approach to Mining Inter-Transaction Fuzzy Association Rules from Stock Price Variation Data

Most of the previous studies on mining association rules focused on mining Boolean intra-transaction associations, i.e., the association rules among binary attributes within the same transaction where the notion of the transaction could be the items bought by the same customer. In this paper, we deal with the problem of mining association rules in databases containing quantitative attributes to discover the associations among different transactions. An example of such an association might be "if company A\´s stock closing price goes up 1% to 3%, company B\´s; price goes up 2% to 4% the next day." In this case, no matter whether we treat company or day as the unit of transaction, the associated items belong to different transactions. However, a problem is caused by sharp boundary in this example. For instance, if A\´s stock closing price goes up only 0.99%, the example we illustrate is not applicable to predict company B\´s; stock price the next day. The fuzzy set concept can help us tackle this kind of problem since fuzzy sets provide a smooth transition between member and non-member of a set. Besides, to mine inter-transaction association rules from the 1-dimensional database, a sliding window concept is introduced. Each sliding window in the database forms a mega-transaction and the associations from these mega-transactions can thus be found. Our algorithm first employs fuzzy set to map quantitative attributes into fuzzy attributes and an a priori-like method is developed to find inter-transaction fuzzy association rules. As compared with conventional methods, more useful results can be found from the proposed fuzzy association rules