Name
Abstract | ||
|---|---|---|
It is a far more time-consuming and expensive task when the number of policy objectives and options is increasing for group decision-making. Based on the semi-supervised clustering, this paper proposes a novel approach to group decision-making. First, the semi-supervised clustering with partially labeled data is introduced as the means of making group decision. Second, the procedure of group decision-making is put forward to identify the optimum scheme and gain the extent of the desired objectives. Finally, a concrete case study is given to indicate the validity and feasibility of this new method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CSSE.2008.860 | CSSE (1) |
Keywords | Field | DocType |
semi-supervised clustering,novel approach,concrete case study,expensive task,group decision,group decision-making,new method,policy objective,optimum scheme,fuzzy set theory,indexes,decision theory,classification algorithms,group decision making,clustering algorithms,cluster analysis,pattern recognition,learning artificial intelligence,mathematical model | Data mining,Pattern clustering,Computer science,Fuzzy set,Decision theory,Artificial intelligence,Labeled data,Cluster analysis,Pattern recognition,Constrained clustering,Statistical classification,Machine learning,Group decision-making | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
1 | ||