The subconscious mined : beneath the surface of a myriad of business data, meaningful patterns can be found by the sophisticated data-mining techniques of neutral networks
| dc.contributor.author | Olivier, Riaan | |
| dc.contributor.author | Gevers, Wim | |
| dc.date.accessioned | 2010-08-26T06:02:37Z | |
| dc.date.available | 2010-08-26T06:02:37Z | |
| dc.date.issued | 2008-02 | |
| dc.description.abstract | The concept of self-organising feature maps (SOFMs) is a powerful tool that can be used in a variety of applications for data-mining and exploration, as well as decision support. In its basic form, an SOFM reduces the parameters that describe a specific application, making it easier for users to understand the underlying problem. By enabling the visualisation of the reduced parameters, it adds the strength of human visual interpretation to complex decision-making scenarios. Applications of these tools may be found in bankruptcy prediction, control of industrial processes and in a variety of other instances with time-series data, as was the case in a study conducted at the University of Stellenbosch Business School (USB). This research examined how SOFMs could help solve the logistical planning of a complex product. | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/4471 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : University of Stellenbosch Business School | en_ZA |
| dc.subject | Self-organising feature maps | en_ZA |
| dc.subject | SOFMs | en_ZA |
| dc.subject | Logistical planning | en_ZA |
| dc.subject | New products -- Planning | en_ZA |
| dc.title | The subconscious mined : beneath the surface of a myriad of business data, meaningful patterns can be found by the sophisticated data-mining techniques of neutral networks | en_ZA |
| dc.type | Article | en_ZA |