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Automation requires computational systems that exhibit some degree of intelligence, in terms of the ability of a system to formulate its own models of the data in question with little or no user intervention. This introductory chapter provides an overview of the content discussed in the subsequent chapters of the book. The book primarily introduces a new approach to the general problem of unsupervised learning, based on the principles of dynamic self-organization. It gives an extensive review of the general problems of unsupervised clustering, with emphasis placed on the inherent relationship that exists between unsupervised learning and self-organization. The book presents self-organizing tree map (SOTM) and its recently successful application in multimedia processing. It describes the developments of the self-organizing hierarchical variance map (SOHVM) and its application in the unsupervised segmentation and visualization of microbiological image data.