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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases
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A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases

机译：挖掘关系数据库对应分析的链接分析扩展

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摘要

This work introduces a link analysis procedure for discovering relationships in a relational database or a graph, generalizing both simple and multiple correspondence analysis. It is based on a random walk model through the database defining a Markov chain having as many states as elements in the database. Suppose we are interested in analyzing the relationships between some elements (or records) contained in two different tables of the relational database. To this end, in a first step, a reduced, much smaller, Markov chain containing only the elements of interest and preserving the main characteristics of the initial chain, is extracted by stochastic complementation [42]. This reduced chain is then analyzed by projecting jointly the elements of interest in the diffusion map subspace [41] and visualizing the results. This two-step procedure reduces to simple correspondence analysis when only two tables are defined, and to multiple correspondence analysis when the database takes the form of a simple star-schema. On the other hand, a kernel version of the diffusion map distance, generalizing the basic diffusion map distance to directed graphs, is also introduced and the links with spectral clustering are discussed. Several data sets are analyzed by using the proposed methodology, showing the usefulness of the technique for extracting relationships in relational databases or graphs.
机译：这项工作介绍了一种链接分析过程，用于发现关系数据库或图形中的关系，从而对简单和多重对应分析进行了概括。它基于通过数据库的随机游走模型，该模型定义了一个马尔可夫链，其状态与数据库中的元素数量一样多。假设我们对分析关系数据库的两个不同表中包含的某些元素（或记录）之间的关系感兴趣。为此，在第一步中，通过随机互补[42]提取了仅包含目标元素并保留初始链主要特征的简化，小得多的马尔可夫链。然后，通过在扩散图子空间[41]中共同投影感兴趣的元素并可视化结果来分析此简化的链。当仅定义两个表时，此两步过程简化为简单的对应关系分析，而当数据库采用简单的星形模式时，则简化为多重对应关系分析。另一方面，还介绍了扩散图距离的内核版本，将基本的扩散图距离概括为有向图，并讨论了具有光谱聚类的链接。使用提出的方法对几个数据集进行了分析，显示了该技术在关系数据库或图形中提取关系的有用性。

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