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Discovering global knowledge from distributed data sources is challenging, where the important issues include the ever-increasing data volume at the highly distributed sources and the general concern on data privacy. Properly abstracting the distributed data with a compact representation which can retain sufficient local details for global knowledge discovery in principle can address both the scalability...
Learning-from-abstraction (LFA) is a recently proposed model-based distributed data mining approach which aims to the mining process both scalable and privacy preserving. However how to set the right trade-off between the abstraction levels of the local data sources and the global model accuracy is crucial for getting the optimal abstraction, especially when the local data are inter-correlated to...
Customer data privacy is known to be a factor which makes just-in-time data sharing and mining among enterprises challenging. Learning-from-abstraction is a recently proposed paradigm for privacy preserving distributed data mining where distributed local data sources are protected by probabilistic data abstraction. In this paper, we investigate the use of a normalized negative log likelihood together...
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