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.

• Type: Conference
• Year: 2006
• Where: ICDE
• Authors: Bee-Chung Chen, Lei Chen 0003, Raghu Ramakrishnan, David R. Musicant
• Keywords: Database, Develop Learning Methods, General Problem Framework, ICDE 2006, Multiple Aggregate Views