Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Top Down FP-Growth for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A New Method for Finding Generalized Frequent Itemsets in Generalized Association Rule Mining
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
FP-tax: tree structure based generalized association rule mining
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
| Hi-index | 0.00 |
The wide existence of taxonomic structures among the attributes of database makes mining generalized association rules an important task. Determining how to utilize the characteristics of the taxonomic structures to improve performance of mining generalized association rules is challenging work. This paper proposes a new algorithm called AFOPT-tax for mining generalized association rules. It projects the transaction database to a compact structure - ascending frequency ordered prefix tree (AFOPT) with a series of optimization, which reduces the high cost of database scan and frequent itemsets generation greatly. The experiments with synthetic datasets show that our method significantly outperforms both the classic Apriori based algorithms and the current FP-Growth based algorithms.