Formal concept analysis and text mining

我们的研究计划涉及两个主要方向。形式概念分析（FCA）和文本挖掘。我们的第一个研究方向是继续在FCA和应用领域的工作，特别是数据挖掘应用。FCA可以被看作是一种层次化的概念聚类方法，它揭示了基于伽罗瓦（或概念）格的形式框架的表格数据中的共性和关系的重要模式。由Wille和我在80年代初开创，这个领域已经大大扩展，在过去的十年里，它已经成为一个完善的学科，具有详细的理论基础，大量的应用程序（数据挖掘，生物信息学，软件工程，信息检索，语言学，心理学，...）和软件工具（商业和开源），以及不断增长的国际社区。在下一个资助期内，我们计划将重点放在以下主题的数据挖掘应用：多级关联规则数据挖掘压缩表示，复杂数据挖掘，近似压缩表示和增量频繁模式挖掘数据仓库的性能优化。

Our research program addresses two main directions. Formal Concept Analysis (FCA) and text mining. Our first research direction is to pursue ongoing work in the field of FCA and applications, in particular data mining applications. FCA can be viewed as a hierarchical conceptual clustering approach that reveals the significant patterns of commonalties and relationships within tabular data based on the formal framework of Galois (or concept) lattices. Pioneered by Wille and myself in the early eighties, this field has largely expanded and it has become over the last ten years a well established discipline with an elaborated theoretical basis, a large array of applications (data mining, bioinformatics, software engineering, information retrieval, linguistics, psychology, ...) and software tools (commercial and open source), and a growing international community. In the next grant period, we plan to focus on data mining applications within the following main themes: multilevel association rule data mining condensed representation, complex data mining, approximate condensed representations and incremental frequent pattern mining for performance optimization in data warehouses.