22nd International Conference on Data Engineering (ICDE'06) Learning from Aggregate Views Atlanta, Georgia April 03-April 07 ISBN: 0-7695-2570-9
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.86
In this paper, we introduce a new class of data mining problems called learning from aggregate views. In contrast to the traditional problem of learning from a single table of training examples, the new goal is to learn from multiple aggregate views of the underlying data, without access to the un-aggregated data. We motivate this new problem, present a general problem framework, develop learning methods for RFA (Restriction-Free Aggregate) views defined using COUNT, SUM, AVG and STDEV, and offer theoretical and experimental results that characterize the proposed methods.
Citation:
Bee-Chung Chen, Lei Chen, Raghu Ramakrishnan, David R. Musicant, "Learning from Aggregate Views," icde, pp.3, 22nd International Conference on Data Engineering (ICDE'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||