Data Mining and Machine Learning Applications

Data Mining and Machine Learning Applications

by Rohit Raja, Kapil Kumar Nagwanshi, Sandeep Kumar, K. Ramya Laxmi
Released March 2022
Publisher(s): Wiley-Scrivener
ISBN: 9781119791782

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Book description

DATA MINING AND MACHINE LEARNING APPLICATIONS

The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration.

Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.

Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth.

The book features:

  • A review of the state-of-the-art in data mining and machine learning,
  • A review and description of the learning methods in human-computer interaction,
  • Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time,
  • The scope and implementation of a majority of data mining and machine learning strategies.
  • A discussion of real-time problems.

Audience

Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. 1 Introduction to Data Mining
    1. 1.1 Introduction
    2. 1.2 Knowledge Discovery in Database (KDD)
    3. 1.3 Issues in Data Mining
    4. 1.4 Data Mining Algorithms
    5. 1.5 Data Warehouse
    6. 1.6 Data Mining Techniques
    7. 1.7 Data Mining Tools
    8. References
  6. 2 Classification and Mining Behavior of Data
    1. 2.1 Introduction
    2. 2.2 Main Characteristics of Mining Behavioral Data
    3. 2.3 Research Method
    4. 2.4 Results
    5. 2.5 Discussion
    6. 2.6 Conclusion
    7. References
  7. 3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects
    1. 3.1 Introduction
    2. 3.2 Related Work on Different Recommender System
    3. References
  8. 4 Stream Mining: Introduction, Tools & Techniques and Applications
    1. 4.1 Introduction
    2. 4.2 Data Reduction: Sampling and Sketching
    3. 4.3 Concept Drift
    4. 4.4 Stream Mining Operations
    5. 4.5 Tools & Techniques
    6. 4.6 Applications
    7. 4.7 Conclusion
    8. References
  9. 5 Data Mining Tools and Techniques: Clustering Analysis
    1. 5.1 Introduction
    2. 5.2 Data Mining Task
    3. 5.3 Data Mining Algorithms and Methodologies
    4. 5.4 Clustering the Nearest Neighbor
    5. 5.5 Data Mining Applications
    6. 5.6 Materials and Strategies for Document Clustering
    7. 5.7 Discussion and Results
    8. References
  10. 6 Data Mining Implementation Process
    1. 6.1 Introduction
    2. 6.2 Data Mining Historical Trends
    3. 6.3 Processes of Data Analysis
    4. References
  11. 7 Predictive Analytics in IT Service Management (ITSM)
    1. 7.1 Introduction
    2. 7.2 Analytics: An Overview
    3. 7.3 Significance of Predictive Analytics in ITSM
    4. 7.4 Ticket Analytics: A Case Study
    5. 7.5 Conclusion
    6. References
  12. 8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques
    1. 8.1 Introduction
    2. 8.2 Literature Review
    3. 8.3 Methodology and Implementation
    4. 8.4 Data Partitioning
    5. 8.5 Conclusions
    6. References
  13. 9 Inductive Learning Including Decision Tree and Rule Induction Learning
    1. 9.1 Introduction
    2. 9.2 The Inductive Learning Algorithm (ILA)
    3. 9.3 Proposed Algorithms
    4. 9.4 Divide & Conquer Algorithm
    5. 9.5 Decision Tree Algorithms
    6. 9.6 Conclusion and Future Work
    7. References
  14. 10 Data Mining for Cyber-Physical Systems
    1. 10.1 Introduction
    2. 10.2 Feature Recovering Methodologies
    3. 10.3 CPS vs. IT Systems
    4. 10.4 Collections, Sources, and Generations of Big Data for CPS
    5. 10.5 Spatial Prediction
    6. 10.6 Clustering of Big Data
    7. 10.7 NoSQL
    8. 10.8 Cyber Security and Privacy Big Data
    9. 10.9 Smart Grids
    10. 10.10 Military Applications
    11. 10.11 City Management
    12. 10.12 Clinical Applications
    13. 10.13 Calamity Events
    14. 10.14 Data Streams Clustering by Sensors
    15. 10.15 The Flocking Model
    16. 10.16 Calculation Depiction
    17. 10.17 Initialization
    18. 10.18 Representative Maintenance and Clustering
    19. 10.19 Results
    20. 10.20 Conclusion
    21. References
  15. 11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining
    1. 11.1 Introduction
    2. 11.2 Background
    3. 11.3 Methodology of CRISP-DM
    4. 11.4 Stage One—Determine Business Objectives
    5. 11.5 Stage Two—Data Sympathetic
    6. 11.6 Stage Three—Data Preparation
    7. 11.7 Stage Four—Modeling
    8. 11.8 Stage Five—Evaluation
    9. 11.9 Stage Six—Deployment
    10. 11.10 Data on ERP Systems
    11. 11.11 Usage of CRISP-DM Methodology
    12. 11.12 Modeling
    13. 11.13 Assessment
    14. 11.14 Distribution
    15. 11.15 Results and Discussion
    16. 11.16 Conclusion
    17. References
  16. 12 Human–Machine Interaction and Visual Data Mining
    1. 12.1 Introduction
    2. 12.2 Related Researches
    3. 12.3 Visual Genes
    4. 12.4 Visual Hypotheses
    5. 12.5 Visual Strength and Conditioning
    6. 12.6 Visual Optimization
    7. 12.7 The Vis 09 Model
    8. 12.8 Graphic Monitoring and Contact With Human–Computer
    9. 12.9 Mining HCI Information Using Inductive Deduction Viewpoint
    10. 12.10 Visual Data Mining Methodology
    11. 12.11 Machine Learning Algorithms for Hand Gesture Recognition
    12. 12.12 Learning
    13. 12.13 Detection
    14. 12.14 Recognition
    15. 12.15 Proposed Methodology for Hand Gesture Recognition
    16. 12.16 Result
    17. 12.17 Conclusion
    18. References
  17. 13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection
    1. 13.1 Introduction
    2. 13.2 Literature Survey
    3. 13.3 Methods and Material
    4. 13.4 Experimental Results
    5. 13.5 Libraries Used
    6. 13.6 Comparing Algorithms Based on Decision Boundaries
    7. 13.7 Evaluating Results
    8. 13.8 Conclusion
    9. References
  18. 14 New Algorithms and Technologies for Data Mining
    1. 14.1 Introduction
    2. 14.2 Machine Learning Algorithms
    3. 14.3 Supervised Learning
    4. 14.4 Unsupervised Learning
    5. 14.5 Semi-Supervised Learning
    6. 14.6 Regression Algorithms
    7. 14.7 Case-Based Algorithms
    8. 14.8 Regularization Algorithms
    9. 14.9 Decision Tree Algorithms
    10. 14.10 Bayesian Algorithms
    11. 14.11 Clustering Algorithms
    12. 14.12 Association Rule Learning Algorithms
    13. 14.13 Artificial Neural Network Algorithms
    14. 14.14 Deep Learning Algorithms
    15. 14.15 Dimensionality Reduction Algorithms
    16. 14.16 Ensemble Algorithms
    17. 14.17 Other Machine Learning Algorithms
    18. 14.18 Data Mining Assignments
    19. 14.19 Data Mining Models
    20. 14.20 Non-Parametric & Parametric Models
    21. 14.21 Flexible vs. Restrictive Methods
    22. 14.22 Unsupervised vs. Supervised Learning
    23. 14.23 Data Mining Methods
    24. 14.24 Proposed Algorithm
    25. 14.25 The Regret of Learning Phase
    26. 14.26 Conclusion
    27. References
  19. 15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier
    1. 15.1 Introduction
    2. 15.2 Related Work
    3. 15.3 Material and Methods
    4. 15.4 Experimental Framework
    5. 15.5 Experimental Results and Discussion
    6. 15.6 Discussion
    7. 15.7 Conclusion
    8. References
  20. 16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques
    1. 16.1 Introduction
    2. 16.2 Related Work
    3. 16.3 Issue and Solution
    4. 16.4 Selection of Data
    5. 16.5 Pre-Preparation Data
    6. 16.6 Application Development
    7. 16.7 Use Case For The Application
    8. 16.8 Conclusion
    9. References
  21. 17 Conclusion and Future Direction in Data Mining and Machine Learning
    1. 17.1 Introduction
    2. 17.2 Machine Learning
    3. 17.3 Conclusion
    4. References
  22. Index
  23. End User License Agreement

Product information

  • Title: Data Mining and Machine Learning Applications
  • Author(s): Rohit Raja, Kapil Kumar Nagwanshi, Sandeep Kumar, K. Ramya Laxmi
  • Release date: March 2022
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119791782