Abstract
In recent years, the National Bureau of Statistics of China released rankings comparing the production of industrial commodities in China and the gross domestic product (GDP) of China to the rest of the world. However, an entry is missing. As data mining for missing attribute values, this paper introduces a new method by combining sequential data mining methods and decision rules theory. By using this method, the missing entry has been lled, which improves Industrial Commodity Statistics Yearbooks.
| Original language | English |
|---|---|
| Pages (from-to) | 6851-6858 |
| Number of pages | 8 |
| Journal | Journal of Computational Information Systems |
| Volume | 9 |
| Issue number | 17 |
| DOIs | |
| Publication status | Published - Oct 29 2013 |
Keywords
- Data mining
- Decision rule
- Decision table
- Gross domestic product
- Information system
- Output of the main industrial commodity
- Ranking
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications