Study on Distributed Sequential Pattern Discovery Algorithm
Study on Distributed Sequential Pattern Discovery Algorithm
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DOI：
10.1360/jos161262
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Zou Xiang
中科院分区：
文献类型：
--
作者：
Zou Xiang
提出算法FDMSP（快速分布式序列模式挖掘）是为了处理分布式环境中的序列模式挖掘问题，并对其性质进行了分析。该算法利用前缀投影技术划分模式搜索空间，利用与前缀相关的轮询站点获取全局支持，并利用局部剪枝、轮询剪枝和计数剪枝来减少候选序列。它被分为三个异步运行的子过程。因此，该算法具有较低的I/O成本、内存成本和通信成本，并且能更高效地生成全局序列模式。实验表明，在将数据集中后，它比GSP算法性能高出68.5%到99.5%，并且在具有大量数据的局域网上具有可扩展性。
Algorithm FDMSP (fast distributed mining of sequential patterns) is proposed in order to deal with mining sequential patterns in distributed environment and its properties are analyzed. The algorithm utilizes prefix-projected technique to divide the pattern searching space, utilizes polling site associated with prefix to get a global support, and utilizes local pruning, poll pruning and count pruning to decrease candidate sequences. It is divided into three sub-procedures which run asynchronously. As a result, the algorithm has lower I/O cost, memory cost and communication cost, and global sequential patterns are generated with higher efficiency. The experiments show that it outperforms the algorithm GSP after centralizing data by 68.5% to 99.5% and scaleable over LAN with huge amount of data.