Incremental Association Rule Mining by Modified Approach of Promising Frequent Itemset Algorithm Based on Bucket Sort Approach

Abstract: The association rule mining is very useful in market basket analysis, web data analysis, decision making, knowing customer trends etc. there are many application in which stream data mining require association rule mining such as web click stream mining, sensor networks, and network traffic analysis. Data streams are continuous, unbounded, usually come with high speed. In incremental association rule mining as the time goes new transaction are added and old transaction are being obsolete. So old rule may be dropped out and new rule may be arrived in. so in this paper I have introduce new approach of incremental association rule mining for finding frequent itemset and promising frequent itemset based on bucket sort algorithm without scanning old database.

Keywords: dynamic database, promising frequent itemset, incremental association rule mining.