This book systematically explores the art and science of Feature Set Partitioning within the MEL framework. We begin by establishing fundamental concepts in Part I: Foundations, where we examine the mathematical underpinnings of partitioning, ensemble learning, and multi-view learning. Part II: Feature Set Partitioning Method Categories constitutes the core of our exploration. Part III: Applications & Challenges bridges theory with practice, examining real-world implementations across diverse domains.