Intelligent Knowledge
A Study beyond Data Mining
Business & Finance, Industries & Professions, Information Management, Business Reference, Business Ethics
| Author: | Yong Shi, Lingling Zhang, Yingjie Tian, Xingsen Li | ISBN: | 9783662461938 |
| Publisher: | Springer Berlin Heidelberg | Publication: | May 8, 2015 |
| Imprint: | Springer | Language: | English |
| Author: | Yong Shi, Lingling Zhang, Yingjie Tian, Xingsen Li |
| ISBN: | 9783662461938 |
| Publisher: | Springer Berlin Heidelberg |
| Publication: | May 8, 2015 |
| Imprint: | Springer |
| Language: | English |
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.
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.
