A Cluster-Based Mining Approach for Mining Fuzzy Association Rules in Two Databases

A Cluster-Based Mining Approach for Mining Fuzzy Association Rules in Two Databases

In this paper, two important issues of mining association rules are investigated. The first problem is the discovery of generalized fuzzy association rules in the transaction database. It’s an important data-mining task, because more general and qualitative knowledge can be uncovered for decision making. However, few algorithms have been proposed in the literature, moreover, the efficiency of these algorithms needs to be improved to handle real-world large datasets. The second problem is to discover association rules from the web usage data and the large itemsets identified in the transaction database. This kind of rules will be useful for marketing decision. In this paper, a cluster-based mining architecture is proposed to address the two problems. At first, an efficient fuzzy association rule miner, based on cluster-based fuzzy-sets tables, is presented to identify all the large fuzzy itemsets. This method requires less contrast to generate large itemsets. Next, a fuzzy rule discovery method is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. An illustrated example is given to demonstrate the effectiveness of the proposed methods.