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Analysis of Social Communities with Iceberg and Stability-Based Concept Lattices

机译:用冰山和基于稳定性的概念格分析社会社区

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In this paper, we presents a research work based on formal concept analysis and interest measures associated with formal concepts. This work focuses on the ability of concept lattices to discover and represent special groups of individuals, called social communities. Concept lattices are very useful for the task of knowledge discovery in databases, but they are hard to analyze when their size become too large. We rely on concept stability and support measures to reduce the size of large concept lattices. We propose an example from real medical use cases and we discuss the meaning and the interest of concept stability for extracting and explaining social communities within a healthcare network.
机译:在本文中,我们提出了一项基于形式概念分析和与形式概念相关的兴趣度量的研究工作。这项工作着重于概念格发现和代表特殊个体群体(称为社会群体)的能力。概念格对于数据库中知识发现的任务非常有用,但是当它们的大小太大时,很难对其进行分析。我们依靠概念稳定性和支持措施来减小大型概念格的大小。我们从实际的医疗用例中提出一个示例,并讨论了概念稳定性在医疗网络中提取和解释社会社区的意义和兴趣。

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