A SURVEY ON MINING UNCERTAIN FREQUENT ITEM SET EFFECTIVELY USING PATTERN GROWTH APPROACH

The Frequent Itemset Mining (FIM) is well-known problem in data mining. The FIM is very useful for business intellisense, weather forecasting etc. Many frequent pattern mining algorithms find patterns from traditional transaction databases, in which the content of each transaction namely, items is definitely known and precise. However, there are many real-life situations in which the content of transactions is uncertain. There are two main approaches for FIM: the level-wise approach and the pattern-growth approach. The level-wise approach requires multiple scans of dataset and generates candidate itemsets. The pattern-growth approach requires a large amount of memory and computation time to process tree nodes because the current algorithms for uncertain datasets cannot create a tree as compact as the original FP-Tree. In this literature the proposed method modifies the tree construction strategy in AT-Mine (Array based Tail node Tree) algorithm. The main goal of the proposed approach is to reduce the total time taken to mine the uncertain Frequent Item set using AT-Mine algorithm