Name
Abstract | ||
|---|---|---|
Mining generalized association rules between items in the presence of the taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or items within the same taxonomy level. This constraint would restrain an expert to discover some more interesting but much less supported association rules. In our, previous work, we have addressed this problem and proposed two algorithms, MMS Cumulate and MMS Stratify. In this paper, we examine the problem of maintaining the discovered multi-support, generalized association rules when new transactions are added into the original database. We propose an algorithm MMS UP. Empirical evaluation showed that MMS UP is 2-6 times faster than running MMS Cumulate or MMS-Stratify on the updated database afresh |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/NAFIPS.2001.943734 | Intell. Data Anal. |
Keywords | Field | DocType |
original database,mms cumulate algorithm,rule maintenance,previous work,taxonomy,taxonomy level,candidate set,empirical evaluation,updated database afresh,mms up algorithm,association rule,database re-scanning,mms stratify algorithm,transaction processing,database theory,generalized association rule,generalized association rule mining,database transactions,data mining,very large databases,large database,multiple minimum | Data mining,Computer science,Association rule learning,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISBN |
3 | 4 | 0-7803-7078-3 |
Citations | PageRank | References |
9 | 0.54 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-cheng Tseng | 1 | 73 | 6.47 |
| Wen-Yang Lin | 2 | 399 | 35.72 |
| Been-Chian Chien | 3 | 10 | 0.91 |