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Exploiting the duality of maximal frequent itemsets and minimal infrequent itemsets for i/o efficient association rule mining (2000).
| Content Provider | CiteSeerX |
|---|---|
| Author | Loo, K. K. Yip, Chi-Lap Kao, Ben Cheung, David |
| Abstract | Any algorithm for mining association rules must discover the set of all maximal frequent itemsets (maxL) from a database. Given a set of itemsets X , to verify that X is maxL, two conditions must be checked: (1) any itemset x in X is frequent, and (2) the dual of X must be the set of all minimal infrequent itemsets (minS). This observation leads us to a family of algorithms for mining association rules. Given a reasonable guess of minS and maxL, we verify their duality relationship, and rene the two sets until the above two conditions hold. We show that previously proposed algorithms such as Apriori, PincerSearch and FindLarge are all members of our algorithm family. Also, we study a member algorithm called FlipFlop. Through a series of experiments, we show that FlipFlop signicantly reduces the I/O requirement of mining association rules. Keywords: Data mining, association rules, lattice 1 Introduction Association rule mining [5] is one of the hottest topics in data minin... |
| File Format | |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Maximal Frequent Itemsets Association Rule Minimal Infrequent Itemsets Efficient Association Rule Mining Data Minin Duality Relationship Member Algorithm Algorithm Family Data Mining Reasonable Guess Introduction Association Rule Mining |
| Content Type | Text |
