document: Parallel mining of closed sequential patterns Discovery of sequential patterns is an essential data mining task with broad applications . Among several variations of sequential patterns , closed sequential pattern is the most useful one since it retains all the information of the complete pattern set but is often much more compact than it . Unfortunately , there is no parallel closed sequential pattern mining method proposed yet . In this paper we develop an algorithm , called Par-CSP ( Parallel Closed Sequential Pattern mining ) , to conduct parallel mining of closed sequential patterns on a distributed memory system . Par-CSP partitions the work among the processors by exploiting the divide-and-conquer property so that the overhead of interprocessor communication is minimized . Par-CSP applies dynamic scheduling to avoid processor idling . Moreover , it employs a technique , called selective sampling to address the load imbalance problem . We implement Par-CSP using MPI on a 64-node Linux cluster . Our experimental results show that Par-CSP attains good parallelization efficiencies on various input datasets .
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