This book provides a thorough introduction to self-organising data mining technologies for business executives, decision makers and specialists involved in developing Executive Information Systems (EIS) or in modelling, data mining or knowledge discovery projects. It is a book for working professionals in many fields of decision making: Economics (banking, financing, marketing), business oriented computer science, ecology, medicine and biology, sociology,
engineering sciences and all other fields of modelling of ill-defined systems.
Each chapter includes some practical examples and a reference list for further reading. This book offers a comprehensive view to all major issues related to self- organising data mining and its practical application for solving real-world problems. It gives not only an introduction to self-organising data mining, but provides answers to questions like:
• what is self-organising data mining compared with other known data mining techniques,
• what are the pros, cons and difficulties of the main data mining approaches,
• what problems can be solved by self-organising data mining, specifically by using the
KnowledgeMiner modelling and prediction tool,
• what is the basic methodology for self-organising data mining and application development
using a set of real-world business problems exemplarily,
• how to use KnowledgeMiner and how to prepare a problem for solution.

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