Practical Data Mining Techniques and Applications

✍ Scribed by Ketan Shah (editor), Neepa Shah (editor), Vinaya Sawant (editor), Neeraj Parolia (editor)

• Publisher: Auerbach Publications
• Year: 2023
• Tongue: English
• Leaves: 215
• Edition: 1
• Category: Library

✦ Synopsis

Data mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users’ goals and mold the algorithms with respect to users’ perspectives.

Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied data science techniques.

By highlighting an overall picture of the field, introducing various mining techniques, and focusing on different applications and research directions using these methods, this book can motivate discussions among academics, researchers, professionals, and students to exchange and develop their views regarding the dynamic field that is data mining.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Contributors
1 Introduction to Data Mining
References
2 Review of Latent Dirichlet Allocation to Understand Motivations to Share Conspiracy Theory: A Case Study of "Plandemic" During COVID-19
2.1 Literature Review
2.1.1 Conspiracy Theories
2.1.2 Measuring Conspiracy Theories Beliefs
2.1.3 Textual Analysis and Text Mining
2.2 Methods
2.2.1 Data Collection
2.2.2 Executing Latent Dirichlet Allocation
2.2.2.1 Dataset Cleaning
2.2.2.2 Dataset Preprocessing
2.2.2.3 Preparing the Data
2.2.2.4 Determining Topics and Corresponding Tweets for the LDA Model
2.2.3 Thematic Analysis
2.3 Results
2.3.1 Results From LDA Tuning Run
2.3.2 Results From Thematic Analysis
2.4 Discussion
2.4.1 Observable Conspiracy Theories Motives
2.4.2 Limitations
2.4.3 Practical Implications and Conclusions
References
3 Near Human-Level Style Transfer
3.1 Introduction
3.2 Methodology
3.3 Pre-Processing
3.4 Feature Extraction Using Transfer Learning
3.5 Performance Parameters
3.6 Content Loss
3.7 Style Loss
3.8 Total Variation Loss
3.9 Optimization
3.10 Super-Resolution
3.11 Results and Implementation
Code
4 Semantics-Based Distributed Document Clustering
4.1 Introduction
4.2 Background and Related Work
4.3 Proposed Approach: Semantics-Based Distributed Document Clustering
4.3.1 Dataset Pre-Processing
4.3.2 Document Representation: Ontology-Based VSM
4.3.3 Distributed K-Means and Bisecting K-Means Algorithm for Document Clustering
4.4 Datasets and Experimental Description
4.4.1 Pre-Processed Datasets
4.4.2 Experimental Setup
4.5 Results and Discussion
4.5.1 Test Cases for Stability of Algorithms (Count of Clusters and Stability)
4.5.2 Test Cases for Accuracy/Quality of Syntactic and Semantic Analysis
4.5.3 Test Cases for Clustering Time
4.6 Conclusion and Future Scope
References
5 Application of Machine Learning in Disease Prediction
5.1 Introduction
5.2 Literature Review
5.3 System Architecture
5.4 Algorithm
5.5 Dataset
5.6 Results and Discussion
5.7 Conclusion
References
6 Federated Machine Learning-Based Bank Customer Churn Prediction
6.1 Introduction
6.2 Related Works
6.3 Dataset
6.4 Experimental Setup
6.5 Proposed Approach
6.6 Results
6.7 Challenges
6.7.1 Costly Communication
6.7.2 Detection of Malicious Clients
6.7.3 Privacy Concern
6.7.4 System Heterogeneity
6.8 Conclusion
References
7 Challenges and Avenues in the Sophisticated Health-Care System
7.1 Introduction
7.2 Organization of the Chapters
7.3 The Challenges Faced By Health-Care Systems
7.3.1 Patients Predictions
7.3.2 Electronic Health Records (EHRs)
7.3.3 Real-Time Alerting
7.3.4 Patient Engagement
7.3.5 Less Use of Health Data for Informed Strategic Planning
7.3.6 Lack of Predictive Analytics in Healthcare
7.3.7 Fraud and Lack of Security
7.3.8 Less Integration of Enormous Data with Medical Imaging
7.3.9 Risk & Disease Management
7.3.10 Increase in Suicide & Self-Harm
7.4 The Technology/Methodology Behind Data Mining
7.5 Conclusion
References
8 Unusual Social Media Behavior Detection Using Distributed Data Stream Mining
8.1 Introduction
8.2 Related Works
8.2.1 User Behavior Analysis
8.2.2 Social Media Bots
8.2.3 Existing Mechanisms/Methods/Algorithms
8.3 Proposed System
8.3.1 Training Models
8.3.2 Unusual Behavior Detection
8.4 Data Format to Be Used
8.5 Conclusion
References
9 Market Basket Analysis Using Distributed Algorithm
9.1 Introduction
9.2 Challenges of DARM
9.3 Literature Work
9.4 Proposed Algorithm: Transaction Reduction Using Enhanced Distributed ARM (TR-EDARM)
9.5 Reduction in Communication Cost Using Efficient Communication in TR-EDARM Algorithm
9.6 Datasets
9.7 Results and Discussion
9.7.1 Experiment 1: Improvement in Execution Time (Comparative Analysis of TR-EDARM with Three Benchmark Algorithms)
9.7.2 Experiment No. 2: Improvement in Communication Cost (Comparative Analysis of CDA, FDM, ODAM, and TR-EDARM Based on Communication Cost)
9.8 Conclusion
References
10 Identification of Crime-Prone Areas Using Data Mining Techniques
10.1 Introduction
10.2 Related Work
10.3 Architecture and Working
10.3.1 Data Collection
10.3.2 Pre-Processing
10.3.3 Model Training and Evaluation Metrics
10.4 Experimental Results
10.4.1 K-Nearest Neighbor
10.4.1.1 Implementing PCA with the KNN Classifier Model
10.4.2 Support Vector Machine
10.4.3 K-Means Clustering
10.4.4 Random Forest
10.5 Data Analysis & Visualization
10.5.1 Identification of Crime Hotspots
10.6 Conclusion
References
11 Smart Baby Cradle for Infant Soothing and Monitoring
11.1 Introduction
11.2 Literature Study
11.3 Proposed Solution
11.3.1 Baby Monitoring System
11.3.1.1 Data Collection Unit
11.3.1.2 Controller Unit— Raspberry Pi B+
11.3.1.3 Baby Soothing System
11.3.2 Data Transfer Unit
11.3.3 Data Analysis Unit
11.3.3.1 Cry Detection Module
11.3.4 Mobile Application
11.3.4.1 App Initialization and First Use
11.3.4.2 Function of the App
11.4 Results
11.4.1 Baby Cry Detection
11.4.2 Mobile Application
11.5 Conclusion
11.6 Limitations
11.7 Future Scope
References
12 Word-Level Devanagari Text Recognition
12.1 Introduction
12.2 Literature Review
12.3 Proposed System
12.3.1 Text Recognizer Application
12.3.2 Processing Server
12.3.2.1 Pre-Processing
12.3.2.2 Feature Extraction and Recognition
12.4 Implementation
12.4.1 Training
12.4.2 Validation
12.4.3 Testing
12.5 Conclusion and Future Scope
References
13 Wall Paint Visualizer Using Panoptic Segmentation
13.1 Introduction
13.2 Related Work
13.3 Methodology
13.3.1 Wall Segmentation
13.3.2 Edge Detection
13.3.3 Colour Replacement
13.3.4 Colour Harmonies
13.4 Results and Discussions
13.5 Conclusion
References
14 Fashion Intelligence: An Artificial Intelligence-Based Clothing Fashion Stylist
14.1 Introduction
14.2 Related Work
14.3 Proposed Method
14.3.1 Adaptive Content Generating Preserving Network
14.3.2 Segmentation Generation
14.3.3 Clothing Image Deformation
14.3.4 Fashion Intelligence Via ALIAS Normalization
14.4 Experiments
14.4.1 Dataset
14.4.2 Qualitative Analysis
14.4.3 Quantitative Analysis
14.5 Conclusion
References
Index