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 Introduction to Data Mining

2 Classification and Mining Behavior of Data

3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

4 Stream Mining: Introduction, Tools & Techniques and Applications

5 Data Mining Tools and Techniques: Clustering Analysis

6 Data Mining Implementation Process

7 Predictive Analytics in IT Service Management (ITSM)

8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques

9 Inductive Learning Including Decision Tree and Rule Induction Learning

10 Data Mining for Cyber-Physical Systems

11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining

12 Human- Machine Interaction and Visual Data Mining

13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection

14 New Algorithms and Technologies for Data Mining

15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier

16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques

17 Conclusion and Future Direction in Data Mining and Machine Learning

About the Authors

Rohit Raja, PhD is an associate professor in the IT Department, Guru Ghasidas Vishwavidyalaya, Bilaspur (CG), India. He has published more than 80 research papers in peer-reviewed journals as well as 9 patents.

Kapil Kumar Nagwanshi, PhD is an associate professor at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, SVKM’s Narsee Monjee Institute of Management Studies Mumbai, India.

Sandeep Kumar, PhD is a professor in the Department of Electronics & Communication Engineering, Sreyas Institute of Engineering & Technology, Hyderabad, India. His area of research includes embedded systems, image processing, and biometrics. He has published more than 60 research papers in peer-reviewed journals as well as 6 patents.

K. Ramya Laxmi, PhD is an associate professor in the CSE Department at the Sreyas Institute of Engineering and Technology, Hyderabad. Her research interest covers the fields of data mining and image processing.