Table of Contents
1 Cover
4 Preface
5 1 Introduction to Data Mining 1.1 Introduction 1.2 Knowledge Discovery in Database (KDD) 1.3 Issues in Data Mining 1.4 Data Mining Algorithms 1.5 Data Warehouse 1.6 Data Mining Techniques 1.7 Data Mining Tools References
6 2 Classification and Mining Behavior of Data 2.1 Introduction 2.2 Main Characteristics of Mining Behavioral Data 2.3 Research Method 2.4 Results 2.5 Discussion 2.6 Conclusion References
7 3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 3.1 Introduction 3.2 Related Work on Different Recommender System References
8 4 Stream Mining: Introduction, Tools & Techniques and Applications 4.1 Introduction 4.2 Data Reduction: Sampling and Sketching 4.3 Concept Drift 4.4 Stream Mining Operations 4.5 Tools & Techniques 4.6 Applications 4.7 Conclusion References
9 5 Data Mining Tools and Techniques: Clustering Analysis 5.1 Introduction 5.2 Data Mining Task 5.3 Data Mining Algorithms and Methodologies 5.4 Clustering the Nearest Neighbor 5.5 Data Mining Applications 5.6 Materials and Strategies for Document Clustering 5.7 Discussion and Results References
10 6 Data Mining Implementation Process 6.1 Introduction 6.2 Data Mining Historical Trends 6.3 Processes of Data Analysis References
11 7 Predictive Analytics in IT Service Management (ITSM) 7.1 Introduction 7.2 Analytics: An Overview 7.3 Significance of Predictive Analytics in ITSM 7.4 Ticket Analytics: A Case Study 7.5 Conclusion References
12 8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 8.1 Introduction 8.2 Literature Review 8.3 Methodology and Implementation 8.4 Data Partitioning 8.5 Conclusions References
13 9 Inductive Learning Including Decision Tree and Rule Induction Learning 9.1 Introduction 9.2 The Inductive Learning Algorithm (ILA) 9.3 Proposed Algorithms 9.4 Divide & Conquer Algorithm 9.5 Decision Tree Algorithms 9.6 Conclusion and Future Work References