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A Metapattern-Based Automated Discovery Loop for Integrated Data Mining-Unsupervised Learning of Relational Patterns
December 1996 (vol. 8 no. 6)
pp. 898-910

Abstract—Metapattern (also known as metaquery) is a new approach for integrated data mining systems. Different from a typical "tool-box" like integration, where components must be picked and chosen by users without much help, metapatterns provide a common representation for intercomponent 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 prelabel 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.

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Index Terms:
Induction, deduction, human interaction, integration, unsupervised learning, relational concepts, metaquery, metapattern.
Citation:
Wei-Min Shen, Bing Leng, "A Metapattern-Based Automated Discovery Loop for Integrated Data Mining-Unsupervised Learning of Relational Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 898-910, Dec. 1996, doi:10.1109/69.553157
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