图模式分析中的格聚类
Clustering with Lattices in the Analysis of Graph Patterns
摘要：
挖掘频繁子图是一个研究领域，我们有一组给定的图（每个图可以被视为一个事务），我们搜索包含在许多这些图中的（连接）子图。在这项工作中，我们将讨论在我们的框架Lattice 2SAR中使用的技术，用于挖掘和分析频繁子图数据及其相应的格信息。格信息由图挖掘算法gSpan提供;它包含频繁子图模式的所有超图-子图关系及其支持。Lattice 2SAR特别用于分析频繁图模式，其中图是分子，频繁子图是片段。在碎片分析中，人们对出现模式的分子感兴趣。这些数据可以是非常广泛的，在本文中，我们专注于一种技术，使其更好地利用我们的聚类中的晶格信息。现在，我们可以减少用户需要访问高度压缩的发生数据的次数。用户不必浏览所有出现数据以搜索在相同分子中出现的模式。相反，人们可以直接看到哪些频繁子图是感兴趣的。
英文摘要：
Mining frequent subgraphs is an area of research where we have a given set of graphs (each graph can be seen as a transaction), and we search for (connected) subgraphs contained in many of these graphs. In this work we will discuss techniques used in our framework Lattice2SAR for mining and analysing frequent subgraph data and their corresponding lattice information. Lattice information is provided by the graph mining algorithm gSpan; it contains all supergraph-subgraph relations of the frequent subgraph patterns -- and their supports. Lattice2SAR is in particular used in the analysis of frequent graph patterns where the graphs are molecules and the frequent subgraphs are fragments. In the analysis of fragments one is interested in the molecules where patterns occur. This data can be very extensive and in this paper we focus on a technique of making it better available by using the lattice information in our clustering. Now we can reduce the number of times the highly compressed occurrence data needs to be accessed by the user. The user does not have to browse all the occurrence data in search of patterns occurring in the same molecules. Instead one can directly see which frequent subgraphs are of interest.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
