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2005
ACM

Parallel mining of closed sequential patterns

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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 a...
Shengnan Cong, Jiawei Han, David A. Padua
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2005
Where KDD
Authors Shengnan Cong, Jiawei Han, David A. Padua
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