A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed Systemts

A scalable and effective algorithm called AMGMSP (approximate mining of global multidimensional sequential patterns) is proposed to solve the problem of mining the multidimensional sequential patterns for large databases in the distributed environment. First, the multidimensional information is embedded into the corresponding sequences in order to convert the mining on the multidimensional sequential patterns to sequential patterns. Then the sequences are clustered, summarized, and analyzed on the distributed sites, and the local patterns could be obtained by the effective approximate sequential pattern mining method. Finally, the global multidimensional sequential patterns could be mined by high vote sequential patterns after collecting all the local patterns on one site. Both the theories and the experiments indicate that this method could simplify the problem of mining the multidimensional sequential patterns and avoid mining the redundant information. The global sequential patterns could be obtained effectively by the scalable method after reducing the cost of communication.