Name
Abstract | ||
|---|---|---|
The quality of data mining results is largely dependent on the ability to accommodate context and user requirements within the mining process. This is done effectively within the pre-processing and presentation stages, however the analysis (or mining) stage remains relatively autonomous and opaque with user input commonly limited to parameter setting. There is, at present, no direct manipulation of the analysis stage which results in the analysis of the domain space being statically constrained. This reduces the quality of results and increases the time needed for analysis. This paper presents a guided association mining environment, GAM, that enhances user-computer synergy by incorporating the user at fine level of granularity within the analysis stage. GAM extends the current state of the art and is based upon a generic guided knowledge discovery environment. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1504/IJBIDM.2007.012944 | IJBIDM |
Keywords | Field | DocType |
association mining environment,mining process,analysis stage,user requirement,user input,knowledge discovery environment,presentation stage,data mining result,direct manipulation,current state,hci,data mining,human computer interaction | Data mining,Quality of results,Computer science,Association mining,Information extraction,Knowledge extraction,Artificial intelligence,Knowledge base,Granularity,User interface,User requirements document,Machine learning | Journal |
Volume | Issue | Citations |
2 | 1 | 3 |
PageRank | References | Authors |
0.38 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron Ceglar | 1 | 106 | 8.42 |
| John F. Roddick | 2 | 1908 | 331.20 |