Titlebook: Big Data Analytics and Knowledge Discovery; 22nd International C Min Song,Il-Yeol Song,Ismail Khalil Conference proceedings 2020 Springer N

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High-Utility Interval-Based Sequencesdered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, traditional frameworks for sequential pattern mining assume all events have the same weight or utility. This simplifying assumption

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Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification high dimensional, dimensionality reduction techniques have been proposed as an efficient approach to lower their dimensionality. One of the most popular dimensionality reduction techniques of time series data is the Symbolic Aggregate Approximation (SAX), which is inspired by algorithms from text m

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Mining Attribute Evolution Rules in Dynamic Attributed Graphsfound in numerous domains, e.g., social network analysis. Several studies have been done on discovering patterns in dynamic attributed graphs to reveal how attribute(s) change over time. However, many algorithms restrict all attribute values in a pattern to follow the same trend (e.g. increase) and

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Sustainable Development Goal Relational Modelling: Introducing the SDG-CAP Methodologysideration the potential relationships between time series associated with individual SDGs, unlike previous work where an independence assumption was made. The challenge is in identifying the existence of relationships and then using these relationships to make SDG attainment predictions. To this en

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https://doi.org/10.1007/978-3-030-59065-9artificial intelligence; association rules; big data; clustering algorithms; computer hardware; computer