An overview of actionable knowledge discovery techniques

2021 ◽  
Author(s):  
Nasrin Kalanat
Keyword(s):  
Knowledge Discovery ◽  
Actionable Knowledge

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2015 ◽  
pp. 241-262
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

scholarly journals Ontology Based Data Analysing Approach for Actionable Knowledge Discovery

2014 ◽  
Vol 16 (6) ◽  
pp. 39-45
Author(s):  
S.Antoinette Aroul Jeyanthi ◽  
◽  
Dr.S Pannirselvam
Keyword(s):  
Knowledge Discovery ◽  
Actionable Knowledge

Actionable Knowledge Discovery

2011 ◽  
pp. 8-13
Author(s):  
Longbing Cao
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Social Intelligence ◽  
Pattern Mining ◽  
Real Life ◽  
Simulated Data ◽  
Business Environment ◽  
Actionable Knowledge ◽  
Real Life Data ◽  
Knowledge Discovery In Database

Actionable knowledge discovery is selected as one of the greatest challenges (Ankerst, 2002; Fayyad, Shapiro, & Uthurusamy, 2003) of next-generation knowledge discovery in database (KDD) studies (Han & Kamber, 2006). In the existing data mining, often mined patterns are nonactionable to real user needs. To enhance knowledge actionability, domain-related social intelligence is substantially essential (Cao et al., 2006b). The involvement of domain-related social intelligence into data mining leads to domaindriven data mining (Cao & Zhang, 2006a, 2007a), which complements traditional data-centered mining methodology. Domain-related social intelligence consists of intelligence of human, domain, environment, society and cyberspace, which complements data intelligence. The extension of KDD toward domain-driven data mining involves many challenging but promising research and development issues in KDD. Studies in regard to these issues may promote the paradigm shift of KDD from data-centered interesting pattern mining to domain-driven actionable knowledge discovery, and the deployment shift from simulated data set-based to real-life data and business environment-oriented as widely predicted.

Flexible Frameworks for Actionable Knowledge Discovery

2010 ◽  
Vol 22 (9) ◽  
pp. 1299-1312 ◽  
Author(s):  
Longbing Cao ◽  
Yanchang Zhao ◽  
Huaifeng Zhang ◽  
Dan Luo ◽  
Chengqi Zhang ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Actionable Knowledge

Domain-Driven, Actionable Knowledge Discovery

2007 ◽  
Vol 22 (4) ◽  
pp. 78-88, c3 ◽  
Author(s):  
Longbing Cao ◽  
Chengqi Zhang ◽  
Qiang Yang ◽  
David Bell ◽  
Michail Vlachos ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Actionable Knowledge
Sign in / Sign up

Export Citation Format

Share Document