Name
Abstract | ||
|---|---|---|
An Informative Rule Set (IRS) is the smallest subset of an association rule set such that it has the same prediction sequence by confidence priority [9]. The problem of maintenance of IRS is a process by which, given a transaction database and its IRS, when the database receives insertion, deletion, or modification, we wish to maintain the IRS as efficiently as possible. Based on the Fast UPdating technique (FUP) [5] for the updating of discovered association rules, we propose here two algorithms to update the discovered IRS when the database is updated by insertion and deletion respectively. Numerical comparisons with the non-incremental informative rule set approach show that our proposed techniques require less computation time, due to less database scanning and less number of candidate rules generated. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.3233/IDA-2007-11305 | Intell. Data Anal. |
Keywords | Field | DocType |
transaction database,fast updating technique,candidate rule,computation time,non-incremental informative rule set,informative ruler set,association rule,confidence priority,approach show,numerical comparison,informative rule set,prediction,data mining | Data mining,Pattern recognition,Information retrieval,Computer science,Association rule learning,Artificial intelligence,Database transaction,Ruler,Computation | Journal |
Volume | Issue | ISSN |
11 | 3 | 1088-467X |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shyue-liang Wang | 1 | 548 | 50.66 |
| Kuan-Wei Huang | 2 | 1 | 1.43 |
| Tien-Chin Wang | 3 | 574 | 30.86 |
| Tzung-pei Hong | 4 | 3768 | 483.06 |