This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the applicationdevelopers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.

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.