An efficient incremental maintenance for association rules mining based on distributed databases

Abstract

Data Mining helps to reveal very important patterns and associations in large databases. Discovering these associations is very important to decision makers. Many organizations have multiple distributed databases and it is important to discover and maintain its association rule. Any update in the database requires starting the whole mining process again to generate new rules. This requires rescanning the whole database to maintain rules. This paper presents an efficient approach to reduce the time of incremental mining for association rules in distributed databases. The proposed approach is Apriori based distributed formulation on message passing interface. It uses the concept of pre large concept to help in reducing the cost of incremental mining. Experiments prove that our proposed incremental approach is much efficient than existing approaches. We present comparative analysis between the proposed approach and existing approach.