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Full-Text Articles in Physical Sciences and Mathematics
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Dissertations
Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.
First, dependencies among discrete, continuous and repeated observations are studied …
Streaming Feature Grouping And Selection (Sfgs) For Big Data Classification, Noura Helal Hamad Al Nuaimi
Streaming Feature Grouping And Selection (Sfgs) For Big Data Classification, Noura Helal Hamad Al Nuaimi
Dissertations
Real-time data has always been an essential element for organizations when the quickness of data delivery is critical to their businesses. Today, organizations understand the importance of real-time data analysis to maintain benefits from their generated data. Real-time data analysis is also known as real-time analytics, streaming analytics, real-time streaming analytics, and event processing. Stream processing is the key to getting results in real-time. It allows us to process the data stream in real-time as it arrives. The concept of streaming data means the data are generated dynamically, and the full stream is unknown or even infinite. This data becomes …