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Big data is becoming a big thing for theoretical and technical innovation and for bigger business, smarter decisions, and bigger economic and social value. This lecture discusses a recently emerging topic: Non-IIDness Learning, which handles two of fundamental challenges in big data analytics: heterogeneity and couplings. Heterogeneity and couplings in big data fundamentally challenge the classic IIDness foundation in statistics, data mining and machine learning etc. As a result, most of existing algorithms and approaches may not work for big data. On the basis of addressing the limitations of existing IIDness-based analysis, mining and learning, the lecture introduces an extended information table based framework for non-IIDness learning, followed by exemplar techniques and case studies for object and pattern relation analysis, such as coupled clustering, coupled ensemble clustering, coupled behavior analysis, and coupling analysis between textual terms, social media items, and patterns to tackle non-IIDness in big data.
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