一种基于标签的链路模式划分策略

Cuifang Zhao, Xiang Zhang, Peng Wang
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引用次数: 2

摘要

随着在线关联数据的爆炸式增长,链接模式的挖掘越来越受到人们的重视。如何在大规模关联数据中高效地进行挖掘是一个实际问题。现有的模式挖掘算法通常假设数据集可以装入主存储器,而数十亿三元组的链接数据远远超出了内存限制。在本文中,我们对一种新的划分策略进行了初步研究,用于挖掘大规模关联数据中的链接模式。首先,我们提出了一种名为Par Group的算法,基于顶点标签对大型链接数据进行划分和分组;其次,我们采用一种自适应的gSpan算法对每个分区中的链接模式进行挖掘,最后将发现的链接模式合并到一个全局结果集中。实验表明,该策略在某些情况下是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Label-Based Partitioning Strategy for Mining Link Patterns
As the explosive growth of online linked data, the task of mining link patterns attracts more and more attention. A practical issue is how to perform mining efficiently in large-scale linked data. Existing pattern mining algorithms usually assume that the dataset can fit into the main memory, while linked data of billion triples is far beyond the memory limitation. In this paper we give a pilot study of a novel partitioning strategy for mining link patterns in large-scale linked data. First, we propose an algorithm named Par Group to divide and group large linked data to partitions based on vertex label, Second, an adapted gSpan is applied for mining link patterns in each partition, At last, discovered link patterns are merged into a global result set. Experiments show that our strategy is feasible and promising in some scenarios.
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