An incremental updating technique is developed for maintenance of the association rules discovered by database mining. There have been many studies on efficient discovery of association rules in large databases. However, it is nontrivial to maintain such discovered rules in large databases because a database may allow frequent or occasional updates and such updates may not only invalidate some existing strong association rules but also turn some weak rules into strong ones. An incremental updating technique is proposed for efficient maintenance of discovered association rules when new transaction data are added to a transaction database.published_or_final_versio
In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a databas...
Data mining is essentially applied to discover new knowledge from a database through an iterative pr...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
A more general incremental updating technique is developed for maintaining the association rules dis...
As new transactions update data sources and subsequently the data warehouse, the previously discover...
Mining association rules among items in a large database have been recognized as one of the most imp...
Abstract- Applying data mining techniques to real-world applications is a challenging task because t...
[[abstract]]Incremental algorithms can manipulate the results of earlier mining to derive the final ...
The association rules represent an important class of knowledge that can be discovered from data war...
As new transactions update data sources and subsequently the data warehouse, the previously discover...
Data mining has recently attracted tremendous amount ofattention in the database research because of...
Abstract—Discover frequent itemsets is the key problem of mining association rules, and the expendit...
In this paper, the issue of mining and maintaining association rules in a large database of customer...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a databas...
In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a databas...
Data mining is essentially applied to discover new knowledge from a database through an iterative pr...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
A more general incremental updating technique is developed for maintaining the association rules dis...
As new transactions update data sources and subsequently the data warehouse, the previously discover...
Mining association rules among items in a large database have been recognized as one of the most imp...
Abstract- Applying data mining techniques to real-world applications is a challenging task because t...
[[abstract]]Incremental algorithms can manipulate the results of earlier mining to derive the final ...
The association rules represent an important class of knowledge that can be discovered from data war...
As new transactions update data sources and subsequently the data warehouse, the previously discover...
Data mining has recently attracted tremendous amount ofattention in the database research because of...
Abstract—Discover frequent itemsets is the key problem of mining association rules, and the expendit...
In this paper, the issue of mining and maintaining association rules in a large database of customer...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a databas...
In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a databas...
Data mining is essentially applied to discover new knowledge from a database through an iterative pr...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...