Formulation of a Multi-relational Data-mining Method by Bottom-up Approach

本研究项目集中于多关系数据挖掘，涉及(1)使用自下而上方法的模式枚举的完整性，(2)过程的完整性和效率之间的权衡，以及(3)在不同领域的适用性，如化学性质识别、药物设计和语料库分析。此外，我们还从理论和实践两个方面阐明了该方法与图挖掘方法的区别，并基于我们提出的基本性质和对这些性质的组合操作设计了一种高效的算法及其变体。该算法被实现为一个挖掘系统，并在各种类型的应用中进行了评估

This research project focused on multi-relational data-mining concerning (1) completeness of enumeration of patterns using the bottom-up approach, (2) trade-off between the completeness and efficiency of the procedure and (3) applicability in various areas, such as identification of chemical properties, drug design and analysis of language corpus. Furthermore, we also clarified differences of the approach from other methods including graph mining approach from theoretical and practical aspects.We designed an efficient algorithm and its variations based on basic properties that we proposed and combination operations for the properties. The algorithm was implemented as a mining system and evaluated in various types of applications