频繁子图挖掘问题的SLS方法

Sidali Hocine Farhi, D. Boughaci
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引用次数: 2

摘要

图挖掘是数据挖掘领域的一个重要研究领域。研究领域集中在图数据集中的频繁子图的识别。针对频繁子图挖掘问题,提出了一种随机局部搜索(SLS)元启发式算法。我们引入了多样化的概念,它包括探索新的邻居解决方案,构成解决困难组合问题的最成功和广泛使用的方法之一。挖掘子图由目标函数定义,目标函数使用两个参数:支持度和大小。size参数的最大化指导查找大型子图。该方法在两个合成数据集和五个不同规模的真实数据集上进行了实现和评估,并与文献中提出的局部搜索(LS)、遗传算法(GA)和可变邻域搜索(VNS)算法进行了比较。该方法利用搜索中的随机性,探索新的解,从而有效地发现搜索空间中多样化的子图。数值结果表明,与LS、GA和VNS方法相比,SLS方法总体上具有较好的解质量和较好的解质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SLS method for the frequent subgraph mining problem (FSM)
Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. This paper proposes a stochastic local search (SLS) meta-heuristic to solve the Frequent Subgraph Mining problem (FSM). We introduce the notion of diversification which consists on exploring new neighbor solutions that constitute one of the most successful and widely used approaches for solving hard combinatorial problems. A mined subgraph is defined by an objective function which uses two parameters, support and size. The maximization of the size parameter directs the search for finding large subgraphs. The proposed method was implemented and evaluated on two synthetic and five real-world datasets of various sizes and compared to the Local Search (LS), Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) algorithms proposed in the literature. The proposed method is able to discover efficiently diversified subgraphs in the search space by exploring new solutions through the use of randomness in the search. The numerical results show that in general SLS method provide competitive results and finds high quality solutions compared to LS, GA and VNS.
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