CrossMine: Efficient Classification Across Multiple Database Relations

CrossMine: Efficient Classification Across Multiple Database Relations (2004)

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by Xiaoxin Yin , Jiawei Han , Jiong Yang , Philip S Yu , Ibm T. J. Watson , Resch Ctr

Venue:	In Proc. 2004 Int. Conf. on Data Engineering (ICDE’04), Boston,MA
Citations:	36 - 11 self

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@INPROCEEDINGS{Yin04crossmine:efficient,
    author = {Xiaoxin Yin and Jiawei Han and Jiong Yang and Philip S Yu and Ibm T. J. Watson and Resch Ctr},
    title = {CrossMine: Efficient Classification Across Multiple Database Relations},
    booktitle = {In Proc. 2004 Int. Conf. on Data Engineering (ICDE’04), Boston,MA},
    year = {2004}
}

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Abstract

Most of today's structured data is stored in relational databases. Such a database consists of multiple relations which are linked together conceptually via entity-relationship links in the design of relational database schemas. Multi-relational classification can be widely used in many disciplines, such as financial decision making, medical research, and geographical applications. However, most classification approaches only work on single "flat" data relations. It is usually difficult to convert multiple relations into a single flat relation without either introducing huge, undesirable "universal relation" or losing essential information. Previous works using Inductive Logic Programming approaches (recently also known as Relational Mining) have proven effective with high accuracy in multi-relational classification. Unfortunately, they suffer from poor scalability w.r.t. the number of relations and the number of attributes in databases.

Citations

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1	is a fellow of the ACM and the IEEE. He is an associate editor of the ACM Transactions on the Internet Technology and the ACM Transactions on Knowledge Discovery in Data. He is a member of the IEEE Data Engineering Steering Committee and is also on the st - Yu