Graph-Based Data Mining

随着当今数据量和复杂性的不断增加，迫切需要加速开发用于挖掘大型数据库的知识发现和概念学习方法。此外，许多数据本质上是结构化的，或者由实体和这些实体之间的关系组成。因此，有必要开发可扩展的方法来发现结构数据库中的新知识。该项目的主要目标是研究和实现在以图表示的结构数据库上执行知识发现和概念学习的新方法。本工作建立在Subdue结构发现系统中实现的基于图的知识发现的现有方法的基础上。将基于图的发现算法扩展到执行结构概念学习和结构分层概念聚类。为了实现更大的可伸缩性，数据库管理技术被集成到基于图的发现和学习过程中。一个目标应用是使用Subdue作为结构化Web搜索引擎的核心。领域专家为分子生物学、地质学、电信和软件工程的应用提供指导和反馈。实现上述目标会影响从不断增加的数据量中自动提取有用知识的能力。通过在Internet上传播Subdue发现算法、数据库和发现的结果，所有领域的科学家都可以从对自己数据库的类似分析中获益。通过将我们的研究理念融入到UTA的课堂教学和学生研究中，这个项目影响了UTA和其他大学的教育。

With the increasing amount and complexity of today's data, there is an urgent need to accelerate the development of knowledge discovery and concept learning methods for mining large databases. Furthermore, much of this data is structural in nature, or is composed of entities and relationships between those entities. Hence, there exists a need to develop scalable methods for discovering new knowledge in structural databases. The main objective of this project is to investigate and implement new methods for performing knowledge discovery and concept learning on structural databases represented as graphs. This work builds upon existing methods for graph-based knowledge discovery implemented in the Subdue structural discovery system. The graph-based discovery algorithm is extended to perform structural concept learning and structural, hierarchical conceptual clustering. To achieve greater scalability, database management techniques are integrated into the graph-based discovery and learning processes. One targeted application is the use of Subdue as the core of a structural Web seach engine. Domain experts provide guidance and feedback on applications to molecular biology, geology, telecommunications, and software engineering. Achievement of the above objectives impacts the ability to automatically extract useful knowledge from the ever-increasing amount of data. By disseminating the Subdue discovery algorithm, databases, and discovered results over the Internet, scientists in all areas benefit from similar analyses of their own databases. Through integration of our research ideas into classes taught at UTA and into student research, this project impacts education at UTA and at other universities.