摘要翻译:
挖掘频繁子图是一个研究领域,我们有一个给定的图集(每个图都可以看作一个事务),我们搜索包含在许多这些图中的(连通的)子图。在这项工作中,我们将讨论在我们的框架Lattice2SAR中用于挖掘和分析频繁子图数据及其相应的格信息的技术。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。Lattice2SAR特别适用于频繁图模式的分析,其中频繁子图是分子,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。这些数据可能非常广泛,在本文中,我们重点研究了一种通过在聚类中使用格信息来使其更好地可用的技术。现在我们可以减少用户需要访问高度压缩的事件数据的次数。用户不必浏览所有发生数据以搜索发生在相同分子中的模式。相反,人们可以直接看到哪些频繁子图是感兴趣的。
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英文标题:
《Clustering with Lattices in the Analysis of Graph Patterns》
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作者:
Edgar H. de Graaf, Joost N. Kok, Walter A. Kosters
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最新提交年份:
2007
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Data Structures and Algorithms 数据结构与算法
分类描述:Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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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.
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PDF链接:
https://arxiv.org/pdf/0705.0593