Name
Title | ||
|---|---|---|
Information-Based pruning for interesting association rule mining in the item response dataset |
Abstract | ||
|---|---|---|
Frequency-based mining of association rules sometimes suffers rule quality problems. In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes. We eliminate noisy and uninformative data using the surprisal first, and then generate association rules of good quality. Experimental results show that the surprisal-based pruning improves quality of association rules in question item response datasets significantly. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11552413_54 | KES (1) |
Keywords | Field | DocType |
frequency-based mining,new measure,information-based pruning,uninformative data,transactional instance,interesting association rule mining,rule quality problem,good quality,surprisal-based pruning,item response dataset,association rule,question item response,association rule mining | Data mining,Computer science,Expert system,Signal-to-noise ratio,Filter (signal processing),Association rule learning,Correlation and dependence,Artificial intelligence,Knowledge base,Transactional leadership,Machine learning,Pruning | Conference |
Volume | ISSN | ISBN |
3681 | 0302-9743 | 3-540-28894-5 |
Citations | PageRank | References |
2 | 0.40 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyeoncheol Kim | 1 | 67 | 16.40 |
| Eun-Young Kwak | 2 | 2 | 0.40 |