Titlebook: Context-Aware Machine Learning and Mobile Data Analytics; Automated Rule-based Iqbal Sarker,Alan Colman,Paul Watters Book 2021 The Editor(s

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Iqbal Sarker,Alan Colman,Paul WattersPresents a comprehensive study and highlights the usefulness of the concept of context-aware machine learning.Introduces an automated rule-based machine learning framework to effectively analyze and d

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A Literature Review on Context-Aware Machine Learning and Mobile Data Analyticscontext-aware machine learning framework presented in the earlier chapter. It covers contextual information in mobile phone data, context discretization, and time-series modeling techniques, rule discovery techniques including association rules and classification rules, dynamic rule updating and man

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Discretization of Time-Series Behavioral Data and Rule Generation based on Temporal Contextf the users, which is used as the basis of generating rules based on temporal context. Although a static segmentation approach is straightforward to comprehend and can be beneficial for analyzing population behavior by comparing across individuals, the generated static segments do not always map to

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Discovering User Behavioral Rules Based on Multi-Dimensional Contextsavioral patterns. In this chapter, we focus on discovering behavioral rules of individual mobile phone users by taking into account multi-dimensional contexts—for example temporal, spatial, or social context. Association rule mining is the most prominent rule-based machine learning method for genera

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Recency-Based Updating and Dynamic Management of Contextual Rulesntexts (temporal, spatial, and social context) utilizing their phone log data. However, user behavior is not static, may change over time in the real world. The discovered rules from mobile phone data, therefore, need to be dynamically updated and managed according to the recent behavioral patterns

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