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和应用领域进行工作，特别是数据挖掘应用。FCA可以被视为一种层次化的概念聚类方法，它基于伽罗瓦(或概念)格的形式框架来揭示表格数据中的共性和关系的重要模式。这个领域是我和Wille在80年代初开创的，在过去的十年里，它已经成为一门成熟的学科，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、心理学等)。和软件工具(商业和开源)，以及不断增长的国际社会。在下一个资助期间，我们计划重点研究以下主题的数据挖掘应用：多层关联规则数据挖掘浓缩表示、复杂数据挖掘、近似浓缩表示和增量频繁模式挖掘，以优化数据仓库的性能。我们的第二个主要研究方向是文本挖掘和信息检索应用。许多可用的电子信息都是文本形式的。网络就是一个明显的例子。文本挖掘关注的是从文本数据中提取有用的表示。这些表示特别用于信息检索和文本分类应用。典型的单项索引和加权方案无法系统地利用同现模式，而同现模式往往是基本概念的更接近的近似。我们正在使用几种方法来提取重要的同现模式。更具体地说，我们的工作利用了遗传算法、人工神经网络和频繁集挖掘算法。

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.Our second main research direction is text mining and information retrieval applications. Much of the available electronic information is in the form of text. The Web is an obvious case. Text mining is concerned with extracting useful representations from textual data. These representations are used in information retrieval and text classification applications in particular. Typical single term indexing and weighting schemes suffer from their inability to systematically exploit co-occurrence patterns that are often more close approximations of the underlying concepts. We are working with several approaches that extract significant co-occurrence patterns. More specifically, our work exploits genetic algorithms, artificial neural networks and frequent set mining algorithms.