MRSM:挖掘具有代表性的极大频繁子图

MRSM:a new algorithm for mining maximal frequent representative subgraphs

基于随机化思想,提出了一种新的挖掘具有代表性的极大频繁子图的算法——MRSM算法。该算法在第一步挖掘极大频繁子图过程中,采用基于随机化的方法,利用已挖掘到的结果,提高算法的效率;在第二步聚类过程中,综合考虑了频繁模式在支持度和结构上的相似性,使得聚类的质量更好。在真实和模拟数据集上的实验结果证实了MRSM算法的有效性。

A new algorithm for maximal frequent representative subgraph mining （MRSM）, called the MRSM algorithm for short, is proposed based on the randomized strategy. The new algorithm uses the mined patterns to improve its efficiency in the stage of mining maximal frequent subgraphs, and in the stage of clustering, it comprehensively considers the similarity in both structure and support of frequent patterns to improve its clustering performance. The extensive experiments on real and synthetic datasets verified the effectiveness and efficiency of the new algorithm, and showed that it can extract high-quality representative patterns.