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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…mehr
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.
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Autorenporträt
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.
Inhaltsangabe
Preface xvii
1 Introduction to Data Mining 1 Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar
1.1. Introduction 1
1.1.1 Data Mining 1
1.2 Knowledge Discovery in Database (KDD) 2
1.2.1 Importance of Data Mining 3
1.2.2 Applications of Data Mining 3
1.2.3 Databases 4
1.3 Issues in Data Mining 6
1.4 Data Mining Algorithms 7
1.5 Data Warehouse 9
1.6 Data Mining Techniques 10
1.7 Data Mining Tools 11
1.7.1 Python for Data Mining 12
1.7.2 KNIME 13
1.7.3 Rapid Miner 17
References 18
2 Classification and Mining Behavior of Data 21 Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala
2.1 Introduction 22
2.2 Main Characteristics of Mining Behavioral Data 23
2.2.1 Mining Dynamic/Streaming Data 23
2.2.2 Mining Graph & Network Data 24
2.2.3 Mining Heterogeneous/Multi-Source Information 25
2.2.3.1 Multi-Source and Multidimensional Information 26
2.2.3.2 Multi-Relational Data 26
2.2.3.3 Background and Connected Data 27
2.2.3.4 Complex Data, Sequences, and Events 27
2.2.3.5 Data Protection and Morals 27
2.2.4 Mining High Dimensional Data 28
2.2.5 Mining Imbalanced Data 29
2.2.5.1 The Class Imbalance Issue 29
2.2.6 Mining Multimedia Data 30
2.2.6.1 Common Applications Multimedia Data Mining 31
2.2.6.2 Multimedia Data Mining Utilizations 31
2.2.6.3 Multimedia Database Management 32
2.2.7 Mining Scientific Data 34
2.2.8 Mining Sequential Data 35
2.2.9 Mining Social Networks 36
2.2.9.1 Social-Media Data Mining Reasons 39
2.2.10 Mining Spatial and Temporal Data 40
2.2.10.1 Utilizations of Spatial and Temporal Data Mining 41
2.3 Research Method 44
2.4 Results 48
2.5 Discussion 49
2.6 Conclusion 50
References 51
3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 57 Rakhi Seth and Aakanksha Sharaff
3.1 Introduction 58
3.2 Related Work on Different Recommender System 60
3.2.1 Challenges in RS 65
3.2.2 Research Questions and Architecture of This Paper 66
3.2.3 Background 68
3.2.3.1 The Architecture of Hybrid Approach 69
3.2.4 Analysis 78
3.2.4.1 Evaluation Measures 78
3.2.5 Materials and Methods 81
3.2.6 Comparative Analysis With Traditional Recommender System 85
