Learning from Aggregate Views

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Learning from Aggregate Views
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.
Bee-Chung Chen, Lei Chen 0003, Raghu Ramakrishnan,
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2006
Where ICDE
Authors Bee-Chung Chen, Lei Chen 0003, Raghu Ramakrishnan, David R. Musicant
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