Name
Abstract | ||
|---|---|---|
There are two directions in data mining research, qualitative analysis and quantitative analysis. Chance Discovery is a useful qualitative analysis method to visualize the data structure and to discover the potential future scenario. But in reality, due to tremendous amount of information, data structure may be too complex for the user to comprehend. In this paper, using Chance Discovery as a basic driving force, we proposed an innovative interactive human-computing process model to extract the data structure of a specific topic that the user is most interested in. Our model combined the strength of both qualitative analysis and quantitative analysis where Grounded theory and text mining technology were applied to sift out meaningful but small data. Experiment results showed that the visualized results generated by our model were more accurate than those obtained by Chance Discovery method. Furthermore, users can evaluate the relevant data structure generated by our model to decide on potential chances. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.ins.2008.11.041 | Inf. Sci. |
Keywords | Field | DocType |
chance discovery method,small data,potential chance,relevant data structure,data mining research,chance discovery,useful qualitative analysis method,quantitative analysis,competitive advantage,qualitative analysis,data structure,qualitative chance discovery,text mining,grounded theory,process model,data mining | Grounded theory,Data structure,Scale-invariant feature transform,Text mining,Small data,Competitive advantage,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
179 | 11 | 0020-0255 |
Citations | PageRank | References |
10 | 1.18 | 5 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao-Fu Hong | 1 | 59 | 15.96 |