TY - GEN
T1 - Mining frequent itemsets using support constraints
AU - Wang, Ke
AU - He, Yu
AU - Han, Jiawei
PY - 2000
Y1 - 2000
N2 - Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Aprio.-i, either misses interesting patterns of low support or suffers from the bottleneck of itemset generation. A better solution is to exploit Support constraints, which specify what minimum support is required for what itemseta, so that only necessary itemsets are generated. In this paper, we present a framework of frequent itemset mining in the presence of support constraints. Our approach is to "push" support constraints into the Apriori it.emset generation so that the "best" minimum support is used for each itemset at run time to preserve the essence of Apriori.
AB - Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Aprio.-i, either misses interesting patterns of low support or suffers from the bottleneck of itemset generation. A better solution is to exploit Support constraints, which specify what minimum support is required for what itemseta, so that only necessary itemsets are generated. In this paper, we present a framework of frequent itemset mining in the presence of support constraints. Our approach is to "push" support constraints into the Apriori it.emset generation so that the "best" minimum support is used for each itemset at run time to preserve the essence of Apriori.
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M3 - Conference contribution
AN - SCOPUS:22044433094
SN - 1558607153
SN - 9781558607156
T3 - Proceedings of the 26th International Conference on Very Large Data Bases, VLDB'00
SP - 43
EP - 52
BT - Proceedings of the 26th International Conference on Very Large Data Bases, VLDB'00
T2 - 26th International Conference on Very Large Data Bases, VLDB 2000
Y2 - 10 September 2000 through 14 September 2000
ER -