A metapattern-based automated discovery loop for integrated data mining-unsupervised learning of relational patterns

摘要

A metapattern (also known as a metaquery) is a new approach for integrated data mining systems. As opposed to a typical "toolbox"-like integration, where components must be picked and chosen by users without much help, metapatterns provide a common representation for inter-component communication as well as a human interface for hypothesis development and search control. One weakness of this approach, however, is that the task of generating fruitful metapatterns is still a heavy burden for human users. In this paper, we describe a metapattern generator and an integrated discovery loop that can automatically generate metapatterns. Experiments in both artificial and real-world databases have shown that this new system goes beyond the existing machine learning technologies, and can discover relational patterns without requiring humans to pre-label the data as positive or negative examples for some given target concepts. With this technology, future data mining systems could discover high-quality, human-comprehensible knowledge in a much more efficient and focused manner, and data mining could be managed easily by both expert and less-expert users.