The subject considers the problems related to data mining techniques and implementation in Big Data environment. The topics include data pre-processing techniques, pattern, association and correlation discovery, classification and clustering, stream and real-time processing techniques and post-processing techniques like outlier detection, statistical and, proximity, and clustering base approaches. Laboratory classes and hands-on programming exercises related to these topics will provide the students with the abilities to design and implement Big Data algorithms and to use already existing software libraries. The subject also addresses the problems of scalability, selection of appropriate implementation techniques, and performance aspects when mining Big Data.
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