基于三元概念分析的相似值双聚类挖掘
Mining Biclusters of Similar Values with Triadic Concept Analysis
摘要：
在2000年初，双聚类数值数据成为一种流行的数据挖掘任务，特别是用于分析基因表达数据。双聚类反映了数值对象/属性数据表中对象子集和属性子集之间的强关联。所谓的相似值的双聚类可以被认为是具有接近值的最大子表。只有少数方法解决了完整的，正确的和非冗余的枚举这样的模式，这是一个众所周知的棘手的问题，而没有正式的框架存在。在本文中，我们介绍了双聚类和形式概念分析之间的重要联系。更具体地说，我们最初表明，三元概念分析（TCA），提供了一个很好的数学框架双聚类。有趣的是，TCA的现有算法，通常适用于二进制数据，可以使用（直接或略有修改）后的预处理步骤，用于提取最大的双聚类相似的值。
英文摘要：
Biclustering numerical data became a popular data-mining task in the beginning of 2000's, especially for analysing gene expression data. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address a complete, correct and non redundant enumeration of such patterns, which is a well-known intractable problem, while no formal framework exists. In this paper, we introduce important links between biclustering and formal concept analysis. More specifically, we originally show that Triadic Concept Analysis (TCA), provides a nice mathematical framework for biclustering. Interestingly, existing algorithms of TCA, that usually apply on binary data, can be used (directly or with slight modifications) after a preprocessing step for extracting maximal biclusters of similar values.
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