Mining circumstance-oriented association rules using singular value decomposition technique

Association rule has evolved from the primitive form of single dimension intratransaction to the form of multi-dimension intertransaction. The challenge for mining multi-dimension intertransaction rules is the formidable search space. Researchers have proposed various methods to handle this problem, such as restricting the number of dimensions, confining search space in a small window, etc. These methods unavoidably have negative impact on mining result and they are less effective when the number of dimensions and the length of rule are really large. Moreover, all these methods are derived from the a priori algorithm and have common weaknesses: time consuming and redundancy caused by the iterative nature of the a priori algorithm. To approach this problem from a different angle, we propose to use the singular value decomposition technique (SVD). With SVD, the multi-dimension intertransaction rules can be easily identified.