Part I Preliminaries -- 1 Introduction to Context-Aware Machine Learning and Mobile Data -- Analytics -- 1.1 Introduction -- 1.2 Context-Aware Machine Learning -- 1.3 Mobile Data Analytics -- 1.4 An Overview of this Book -- 1.5 Conclusion -- References -- 2 Application Scenarios and Basic Structure for Context-Aware -- Machine Learning Framework -- 2.1 Motivational Examples with Application Scenarios -- 2.2 Structure and Elements of Context-Aware Machine Learning -- Framework -- 2.2.1 Contextual Data Acquisition -- 2.2.2 Context Discretization -- 2.2.3 Contextual Rule Discovery -- 2.2.4 Dynamic Updating and Management of Rules -- 2.3 Conclusion -- References -- 3 A Literature Review on Context-Aware Machine Learning and -- Mobile Data Analytics -- 3.1 Contextual Information -- 3.1.1 Definitions of Contexts -- 3.1.2 Understanding the Relevancy of Contexts -- 3.2 Context Discretization -- 3.2.1 Discretization of Time-Series Data -- 3.2.2 Static Segmentation -- vii -- viii Contents -- 3.2.3 Dynamic Segmentation -- 3.3 Rule Discovery -- 3.3.1 Association Rule Mining -- 3.3.2 Classification Rules -- 3.4 Incremental Learning and Updating -- 3.5 Identifying the Scope of Research -- 3.6 Conclusion -- References -- Part II Context-Aware Rule Learning and Management -- 4 Contextual Mobile Datasets, Pre-processing and Feature Selection -- 4.1 Smart Mobile Phone Data and Associated Contexts -- 4.1.1 Phone Call Log -- 4.1.2 Mobile SMS Log -- 4.1.3 Smartphone App Usage Log -- 4.1.4 Mobile Phone Notification Log -- 4.1.5 Web or Navigation Log -- 4.1.6 Game Log -- 4.1.7 Smartphone Life Log -- 4.1.8 Dataset Summary -- 4.2 Examples of Contextual Mobile Phone Data -- 4.2.1 Time-Series Mobile Phone Data -- 4.2.2 Mobile phone data with multi-dimensional contexts -- 4.2.3 Contextual Apps Usage Data -- 4.3 Data Preprocessing -- 4.3.1 Data Cleaning -- 4.3.2 Data Integration -- 4.3.3 Data Transformation -- 4.3.4 Data Reduction -- 4.4 Dimensionality Reduction -- 4.4.1 Feature Selection -- 4.4.2 Feature Extraction -- 4.4.3 Dimensionality Reduction Algorithms -- 4.5 Conclusion -- References -- 5 Discretization of Time-Series Behavioral Data and Rule Generation -- based on Temporal Context -- 5.1 Introduction -- 5.2 Requirements Analysis -- 5.3 Time-series Segmentation Approach -- 5.3.1 Approach Overview -- 5.3.2 Initial Time Slices Generation -- 5.3.3 Behavior-Oriented Segments Generation -- Contents ix -- 5.3.4 Selection of Optimal Segmentation -- 5.3.5 Temporal Behavior Rule Generation using Time Segments -- 5.4 Effectiveness Comparison -- 5.5 Conclusion -- References -- 6 Discovering User Behavioral Rules based on Multi-dimensional -- Contexts -- 6.1 Introduction -- 6.2 Multi-dimensional Contexts in User Behavioral Rules -- 6.3 Requirements Analysis -- 6.4 Rule Mining Methodology -- 6.4.1 Identifying the Precedence of Context -- 6.4.2 Designing Association Generation Tree -- 6.4.3 Extracting Non-Redundant Behavioral Association Rules -- 6.5 Experimental Analysis -- 6.5.1 Effect on the Number of Produced Rules -- 6.5.2 Effect of Confidence Preference the Predicted Accuracy -- 6.5.3 Effectiveness Comparison -- 6.6 Conclusion -- References -- 7 Recency-based Updating and Dynamic Management of Contextual -- Rules -- 7.1 Introduction -- 7.2 Requirements Analysis -- 7.3 An Example of Recent Data -- 7.4 Identifying Optimal Period of Recent Log Data -- 7.4.1 Data Splitting -- 7.4.2 Association Generation -- 7.4.3 Score Calculation -- 7.4.4 Data Aggregation -- 7.5 Machine Learning based Behavioral Rule Generation and Management -- 7.6 Effectiveness Comparison and Analysis -- 7.7 Conclusion -- References -- Part III Application and Deep Learning Perspective -- 8 Context-Aware Rule-based Expert System Modeling -- 8.1 Structure of a Context-Aware Mobile Expert System -- 8.2 Context-Aware Rule Generation Methods -- 8.3 Context-Aware IF-THEN Rules and Discussion -- 8.3.1 IF-THEN Classification Rules -- 8.3.2 IF-THEN Association Rules -- x Contents -- 8.4 Conclusion -- References -- 9 Deep Learning for Contextual Mobile Data Analytics -- 9.1 Introduction -- 9.2 Contextual Data -- 9.3 Deep Neural Network Modeling -- 9.3.1 Model Overview -- 9.3.2 Input Layer -- 9.3.3 Hidden Layer(s) -- 9.3.4 Output Layer -- 9.4 Prediction Results of the Model -- 9.5 Conclusion -- References -- 10 Context-Aware Machine Learning System: Applications and -- Challenging Issues -- 10.1 Rule-based Intelligent Mobile Applications -- 10.2 Major Challenges and Research Issues -- 10.3 Concluding Remarks -- References.