Name
Abstract | ||
|---|---|---|
Ordered information is a kind of useful background knowledge to guide a discovery process toward finding different types of novel rules and improving their quality for many real world data mining tasks. In the paper, we investigate ways of using ordered information for gastric cancer data mining, based on rough set theory and granular computing. With respect to the notion of ordered information tables, we describe how to mine ordering rules and how to form granules of values of attributes in a pre/post-processing step for improving the quality of the mined classification rules. Experimental results in gastric cancer data mining show the usefulness and effectiveness of our approaches. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45813-1_62 | Rough Sets and Current Trends in Computing |
Keywords | Field | DocType |
ordered information,mined classification rule,novel rule,real world data mining,granular computing,gastric cancer data mining,information table,discovery process,different type,data mining,rough set theory | Data mining,Computer science,Rough set,Information extraction,Granular computing,Business process discovery | Conference |
Volume | ISSN | ISBN |
2475 | 0302-9743 | 3-540-44274-X |
Citations | PageRank | References |
6 | 0.52 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Zhong | 1 | 2907 | 300.63 |
| Juzhen Dong | 2 | 214 | 17.05 |
| Y. Y. Yao | 3 | 9707 | 674.28 |
| Setsuo Ohsuga | 4 | 960 | 222.02 |