Name
Abstract | ||
|---|---|---|
This paper presents the advances in subgroup discovery and the ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allow the decision maker to recognize some important relations and to perform an appropriate action, such as planning a population screening campaign aimed at detecting individuals with high disease risk. Two case studies from medicine and functional genomics are used to present the lessons learned in solving problems requiring actionable knowledge generation for decision support. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11430919_2 | PAKDD |
Keywords | Field | DocType |
actionable knowledge generation,subgroup discovery,decision support,subgroup discovery technique,explicit symbolic knowledge,appropriate action,actionable knowledge,high disease risk,case study,functional genomics,decision maker | Decision rule,Population,Data mining,Knowledge generation,Computer science,Decision support system,Association rule learning,Knowledge extraction,Decision maker | Conference |
Volume | ISSN | ISBN |
3518 | 0302-9743 | 3-540-26076-5 |
Citations | PageRank | References |
7 | 0.57 | 9 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nada Lavrač | 1 | 989 | 72.19 |