Name
Abstract | ||
|---|---|---|
Mining generalized association rules 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 support to be uniformly specified for all items or for items within the same taxonomy level. In this paper, we extended the scope of mining generalized association rules in the presence of taxonomy to allow any form of user-specified multiple minimum supports. We discussed the problems of using classic Apriori itemset generation and presented two algorithms, MMS_Cumulate and MMS_Stratify, for discovering the generalized frequent itemsets. Empirical evaluation showed that these two algorithms are very effective and have good linear scale-up characteristic. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44801-2_2 | DaWaK |
Keywords | Field | DocType |
mining generalized association rules,user-specified multiple minimum,data mining,minimum support,generalized frequent itemsets,taxonomy level,multiple minimum supports,important model,classic apriori itemset generation,generalized association rule,empirical evaluation,good linear scale-up characteristic,cumulant | Data mining,Computer science,A priori and a posteriori,Association rule learning,Information extraction | Conference |
ISBN | Citations | PageRank |
3-540-42553-5 | 15 | 0.78 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-cheng Tseng | 1 | 73 | 6.47 |
| Wen-Yang Lin | 2 | 399 | 35.72 |