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Tree Based Reduction of Concept Lattices Based on Conceptual Indexes

机译:基于概念索引的基于树减少概念格子

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There are many approaches and tools which deal with conceptual structures in datasets and their main goal is to support user in understanding of data and structure. One of methods is formal concept analysis (FCA) which is suitable for processing and analyzing input data of object-attributes models based on their relationship. One from FCA family is model of generalized one-sided concept lattice (GOSCL). It is suitable to work with different type of attributes. While generating one-sided concept lattices in FCA improved understanding and interpretation of analysis, one of the lasting problem is to provide the users a result of FCA in appropriate form, if there is large number of concept lattices and generated structure is complex. This is one of the main topics in the FCA and solution can be reached with the reduction methods. In this paper we propose some of the reduction techniques and their combinations.
机译:有许多方法和工具,处理数据集中的概念结构及其主要目标是支持用户理解数据和结构。方法之一是正式概念分析(FCA),适用于根据其关系处理和分析对象属性模型的输入数据。来自FCA系列的一个是广义单面概念格子(GOSCL)的模型。它适合使用不同类型的属性。在FCA中生成单面概念格子的虽然改善了对分析的理解和解释,但持久的问题之一是向用户以适当的形式提供FCA的结果,如果有大量的概念格和生成的结构是复杂的。这是FCA中的主要主题之一,可以通过减少方法达到解决方案。在本文中,我们提出了一些减少技术及其组合。

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