Nutan Sarode, Devendra Gadekar
Abstract: Data mining has been around and all enterprises in the real world need it in order to make well informed decisions. The reason behind this is that analyzing huge data is not possible manually. For mining high utility item sets from databases many techniques came into existence. The discovery of item sets with high utility like profits is referred by mining high utility item sets from a transactional database.A number of data mining algorithms have been proposed, for high utility item sets the problem of producing a large number of candidate item sets is incurred. The mining performance is degraded by such a large number of candidate item sets in terms of execution time and space requirement. There are many Problems Occurs when the database contains lots of long transactions or long high utility item sets. Internet purchasing and transactions is increased in recent years, mining of high utility item sets especially from the big transactional databases is required task to process many day to day operations in quick time. Mining high utility item sets from a transactional database means to retrieve high utility item sets from database.Which item sets have highest profit known as High utility item sets. In existing system number of Algorithms have been proposed but there is problem like it generate huge set of candidate Item sets for High Utility Item sets.Existing UP-Growth and UP-Growth+ usedf with aim of improving the performances of high utility itemsts.We will compare the performances of existing algorithms UP-Growth and UP-Growth+ against the improve UP-Growth and UP-Growth+.
Keywords: Data mining, high utility item sets, candidate pruning, frequent item sets, Utility Mining