Applied Cluster Analysis - Part I

<p>This book is primarily designed for engineers developers researchers and data scientists to explore a range of ideas and approaches to unsupervised machine learning techniques and cluster analysis offering fresh insights rigorous understanding and clear perspectives. While the various bodies of knowledge mention some of the topics discussed in this book the several example applications new reflections and developments offer new insights and perspectives on cluster analysis. This book’s content includes a mathematical description of the theory behind to unsupervised machine learning techniques and cluster analysis extensive example applications explaining all theories and assumptions of cluster analysis the most popular clustering models and their importance their alternate applications the associated distance measures and parameter choices and a description and discussion of popular methods used in evaluating clustering quality and validating clusters with several example applications. This book provides a comprehensive examination of common challenges encountered in determining the number of clusters along with guidance on addressing them. It focuses on both hierarchical and non-hierarchical clustering methods 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 on how to classify them accurately. 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 cluster analysis problems effectively. With this book the readers will explore the theory and application concepts essential for mastering the unsupervised machine learning concepts. </p>