This book is mainly about an innovative and fundamental method called "e;intelligent knowledge"e; to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the "e;first-order"e; analytic process, "e;second-order"e; analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.

This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.

​Dedication.- Preface.- Data Mining and Knowledge Management.- Foundations of Intelligent Knowledge Management.- Intelligent Knowledge and Habitual Domain.- Domain Driven Intelligent Knowledge Discovery.- Knowledge-Incorporated Multiple Criteria Linear Programming Classifiers.- Knowledge Extraction from Support Vector Machines.- Intelligent Knowledge Acquisition and Application in Customer Churn.- Intelligent Knowledge Management in Expert Mining in Traditional Chinese Medicines.