Name
Title | ||
|---|---|---|
Data mining for decision support on customer insolvency in telecommunications business |
Abstract | ||
|---|---|---|
This paper reports on the findings of a research project that had the objective to build a decision support system to handle customer insolvency for a large telecommunication company. Prediction of customer insolvency, well in advance, and with an accuracy that could make this prediction useful in business terms, was one of the core objectives of the study. In the paper the process of building such a predictive model through knowledge discovery and data mining techniques in vast amounts of heterogeneous as well as noisy data is described. The reported findings are very promising, making the proposed model a useful tool in the decision making process, while some of the discussed problems and limitations are of interest to researchers who intend to use data mining approaches in other similar real-life problems. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0377-2217(02)00532-5 | European Journal of Operational Research |
Keywords | Field | DocType |
Decision support systems,Predictive data mining,Classification,Customer insolvency | Data science,Data mining,Telecommunications,Intelligent decision support system,Decision support system,Insolvency,Business decision mapping,Knowledge extraction,R-CAST,Decision engineering,Decision-making,Mathematics | Journal |
Volume | Issue | ISSN |
145 | 2 | 0377-2217 |
Citations | PageRank | References |
28 | 2.00 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sophia Daskalaki | 1 | 295 | 18.52 |
| Ioannis Kopanas | 2 | 76 | 5.58 |
| M. Goudara | 3 | 28 | 2.00 |
| Nikolaos Avouris | 4 | 968 | 94.62 |