Learning from Aggregate Views

Authors:
Bee-Chung Chen;Lei Chen;Raghu Ramakrishnan;David R. Musicant
Affiliations:
University of Wisconsin;University of Wisconsin;University of Wisconsin;Carleton College
Venue:
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Year:
2006

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Abstract

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.