Name
Abstract | ||
|---|---|---|
Traditional data mining predictive algorithm dealt little with original data set and did not make full use of the relationship of the data, as a result, numerous mathematical operation resources was wasted and also the accuracy of the predicted result was not very high. Against with this problem, correlation coefficient and information entropy were introduced, a data mining algorithmic based on information entropy was put forward, and an incremental predictive algorithm had been realized, too. Because the algorithm makes full use of the data sets’ interior relationship, it makes the forecasted results more accurate, and achieves a satisfied result. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CSSE.2008.551 | CSSE (4) |
Keywords | Field | DocType |
satisfiability,couplings,correlation,information entropy,predictive models,data mining,entropy,prediction algorithms | Data correlation,Data mining,Correlation coefficient,Operation,Computer science,Prediction algorithms,Artificial intelligence,Entropy (information theory),Machine learning | Conference |
Volume | Issue | Citations |
4 | null | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dingsheng Wan | 1 | 97 | 8.76 |
| Xiang Ren | 2 | 885 | 60.08 |
| Yuting Hu | 3 | 2 | 1.37 |