Description
| Product ID: | 9781441962867 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | US |
| Series: | Annals of Information Systems |
| Title: | Data Mining for Social Network Data |
| Authors: | Author: David L. Hicks, Nasrullah Memon, Jennifer Jie Xu, Hsinchun Chen |
| Page Count: | 216 |
| Subjects: | Business mathematics and systems, Business mathematics & systems, Business applications, Pattern recognition, Business applications, Pattern recognition |
| Description: | Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations.Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world. |
| Imprint Name: | Springer-Verlag New York Inc. |
| Publisher Name: | Springer-Verlag New York Inc. |
| Country of Publication: | GB |
| Publishing Date: | 2010-07-09 |
