Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. In recent years, the problem of high utility pattern mining become one of the most important research area in data mining. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. The existing high utility mining algorithm generates large number of candidate itemsets, which takes much time to find utility value of all candidate itemsets, especially for dense datasets. In this paper we have proposed UP-tree structure to reduce number of PHUIs(Potentially High Utility Itemsets ) and to reduce execution time in Incremental High Utility Pattern Mining(IHUP). This algorithm has two strategies which is compared with other existing algorithms in various aspects. The experimental results show that the proposed algorithms reduce the number of candidates effectively.
Keywords
Incremental High Utility Pattern Mining
Dense Database
Potentially High Utility Itemsets.
Authors
S.Dhivya
T.T.Mathangi
S.Jeniba
How to Cite this Article
S.Dhivya, T.T.Mathangi, S.Jeniba (2016).
"UP-TREE STRUCTURE FOR POTENTIALLY HIGH UTILITY ITEMSETS".
International Journal of Contemporary Research in Computer Science and Technology,
2(12), pp. 1104-1107.
