Applied Cluster Analysis - Part I

Primarily designed for engineers, developers, researchers, and data scientists, this book provides a range of ideas, fresh insights, rigorous understanding, and clear perspectives on cluster analysis. Several example applications provide new insights, developments, and perspectives on the practical application of unsupervised machine learning techniques. This book's content includes a mathematical description of the theory behind unsupervised machine learning techniques, explaining all theories and assumptions with examples. This book further describes the most popular clustering models, their importance, alternative applications, the associated distance measures and parameter choices, and the popular methods used in evaluating clustering quality. A detailed examination is provided on the common challenges encountered in determining the number of clusters, along with guidance on addressing them, guiding the user through complex concepts with clear and concise explanations. Additionally, the contents include details on the common types of clusters found in real-life datasets, guiding readers in accurately classifying them. Adequate explanations and examples of applications using R and SPSS are provided throughout the book, guiding users through complex concepts and problem-solving with clarity and precision. The structured guidance helps deepen one's theoretical knowledge and increase the ability to solve practical cluster analysis problems. With this book, readers can explore the theory and application concepts essential for mastering unsupervised machine learning. This book is the best guide for anyone who wants to learn to use statistics and unsupervised machine learning techniques for data analysis and data mining, including researchers who apply statistics and unsupervised machine learning methods in their research, business professionals such as project managers, and market analysts who want to explore and analyze their company data.