Domain-Driven, Actionable Knowledge Discovery

Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.

Authors

• Longbing Cao Macquarie University
• Chengqi Zhang Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China
• David Bell
• Michail Vlachos
• Bahar Taneri
• Eamonn Keogh
• Philip S. Yu University of Illinois, Chicago
• Ning Zhong
• Mafruz Zaman Ashrafi
• David Taniar
• Eugene Dubossarsky
• Warwick Graco

Keywords

• Data mining
• Data visualization
• Humans
• Data privacy
• Intelligent networks
• Government
• Data security
• Intelligent systems
• Research and development
• Machine vision
• Actual Knowledge
• Knowledge Discovery
• Brain Activity
• Level Of Knowledge
• Decision Tree
• Visual System
• Chimpanzees
• Inductive Reasoning
• Domain Experts
• Intelligence Algorithms
• Local Frequency
• Interesting Technique
• Global Frequency
• Video System
• Data Mining Algorithms
• Multi-party Computation
• Business Knowledge
• Mining Models
• Hippopotamus
• Data Mining Models
• Frequent Itemsets
• Multi-phase Process
• Exact Frequency
• Cognitive Domains
• Communication Cost
• Orangutans
• data models
• database searching
• knowledge engineering
• visualization