4 Clustering and association algorithm
-
M. Ashwin
Abstract
This chapter provides an extensive examination of clustering and association algorithms, offering a comprehensive understanding of these essential unsupervised learning techniques in the field of machine learning. The chapter commences with an introductory section that underscores the significance of unsupervised learning, and presents a detailed overview of clustering and association algorithms. The various clustering algorithms covered in the following sections are k-means, hierarchical clustering, densitybased clustering (like DBSCAN), and Gaussian mixture models. Additionally, the chapter explores evaluation metrics for clustering, including both internal and external evaluation metrics, and offers advise on how to properly interpret the evaluation results. The chapter also examines dimensionality reduction methods, particularly principal component analysis and t-SNE, and clarifies how to use them in clustering scenarios. The Apriori algorithm, frequent itemset mining, association rule development, and methods for judging rule interestingness are all covered in the section on association rule learning. Along with explaining their individual uses, the chapter also goes into advanced clustering methods like density-based spatial clustering, mean-shift clustering, spectral clustering, and affinity propagation. The chapter also discusses association rule mining, ensemble clustering, and time series clustering in huge datasets. The chapter concludes with an overview of the core ideas and developments in unsupervised learning, along with a list of the field’s present research roadblocks and potential future paths.
Abstract
This chapter provides an extensive examination of clustering and association algorithms, offering a comprehensive understanding of these essential unsupervised learning techniques in the field of machine learning. The chapter commences with an introductory section that underscores the significance of unsupervised learning, and presents a detailed overview of clustering and association algorithms. The various clustering algorithms covered in the following sections are k-means, hierarchical clustering, densitybased clustering (like DBSCAN), and Gaussian mixture models. Additionally, the chapter explores evaluation metrics for clustering, including both internal and external evaluation metrics, and offers advise on how to properly interpret the evaluation results. The chapter also examines dimensionality reduction methods, particularly principal component analysis and t-SNE, and clarifies how to use them in clustering scenarios. The Apriori algorithm, frequent itemset mining, association rule development, and methods for judging rule interestingness are all covered in the section on association rule learning. Along with explaining their individual uses, the chapter also goes into advanced clustering methods like density-based spatial clustering, mean-shift clustering, spectral clustering, and affinity propagation. The chapter also discusses association rule mining, ensemble clustering, and time series clustering in huge datasets. The chapter concludes with an overview of the core ideas and developments in unsupervised learning, along with a list of the field’s present research roadblocks and potential future paths.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
