双聚类方法的统一分类
Towards a Unified Taxonomy of Biclustering Methods
摘要：
作为一种无监督的机器学习和数据采掘技术，双聚类及其多模态扩展正成为分析不同领域对象-属性数据的流行工具。除了传统的聚类技术，双聚类是搜索同质的对象组，同时保持它们的共同描述，例如，在二进制设置中，它们的共享属性。在生物信息学中，双聚类被用来寻找基因，这些基因在一个子集的情况下是活跃的，因此是生物标志物的候选者。然而，那些在基因表达分析中流行的双聚类技术的作者可能会忽略现有的方法。例如，BiMax算法旨在寻找双聚类，这是几十年来众所周知的形式概念。此外，即使生物信息学根据合理的领域驱动标准对双聚类方法进行分类，它们的分类分类法也可能因调查而异，并且也不完整。因此，在本文中，我们建议使用概念格作为分类法的建设（在双聚类域）和属性探索的跨域分类完成的手段工具。
英文摘要：
Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used to find genes, which are active in a subset of situations, thus being candidates for biomarkers. However, the authors of those biclustering techniques that are popular in gene expression analysis, may overlook the existing methods. For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts. Moreover, even if bioinformatics classify the biclustering methods according to reasonable domain-driven criteria, their classification taxonomies may be different from survey to survey and not full as well. So, in this paper we propose to use concept lattices as a tool for taxonomy building (in the biclustering domain) and attribute exploration as means for cross-domain taxonomy completion.
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