Efficient Mining High Utility Item sets from Transactional Databases using UP-Growth and UP-Growth+ Algorithm

One of the important research area in data mining is high utility pattern mining. Discovering itemsets with high utility like profit from database is known as high utility itemset mining. There are number of existing algorithms have been work on this issue. Some of them incurs problem of generating large number of candidate itemsets. This leads to degrade the performance of mining in case of execution time and space. In this paper we have focus on UP-Growth and UP-Growth+ algorithm which overcomes this limitation. This technique uses tree based data structure, UP-Tree for generating candidate itemsets with two scan of database. In this paper we extend the functionality of these algorithms on transactional database. Discovering itemsets with high utility like profitable items from database is known as high utility itemset mining. There are many number of existing algorithms have been work on this issue. But some of them incurs problem of generating large number of candidate itemsets. This affects to degrade the performance of mining in case of execution time and space. In this paper we have focus on UP-Growth and UP-Growth+ algorithm which will overcome this limitation. This technique uses tree based data structure finding itemsets, UP-Tree for generating candidate itemsets with two scan of database. In this paper we extend the functionality of UP-Growth and UP-Growth+ algorithms on transactional database. In High utility itemsets mining the objective is to identify itemsets that have utility value above a given utility threshold to generate tree.