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　　择要 　　转自：郑宇MSRA　　跨域数据融合全套PPT分章节全部公开（300MB+），算法连合 案例，助力攻克数据发掘 和呆板 学习新难点，抢占大数据和人工智能制高点。　　1. Overview　　Traditional data mining usually deals with data from a single domain. In the big data era, we face a diversity of datasets from different sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. How to unlock the power of knowledge from multiple disparate (but potentially connected) datasets is paramount in big data research, essentially distinguishing big data from traditional data mining tasks. This calls for advanced techniques that can fuse the knowledge from various datasets organically in a machine learning and data mining task. These methods focus on knowledge fusion rather than schema mapping and data merging, significantly distinguishing between cross-domain data fusion and traditional data fusion studied in the database community.