Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from adata-centered pattern mininga to adomain driven actionable knowledge discoverya for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.The first category is manual techniques and usually involve great efforts by users, e.g. questionnaire and interview. The downside of ... The proposed model starts to ask users provide a query to access the ontology in order to capture their information needs at the concept level. Our model ... need. They also classified Web user profiles into two diagrams: the data diagram and information diagram. A dataanbsp;...
|Title||:||Data Mining for Business Applications|
|Author||:||Longbing Cao, Philip S. Yu, Chengqi Zhang, Huaifeng Zhang|
|Publisher||:||Springer Science & Business Media - 2008-10-03|