Utility Mining Algorithm for High Utility Item sets from Transactional Databases
The discovery of item sets with high utility like profits is referred by mining high utility item sets from a transactional database. Although in recent years a number of relevant 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. When the database contains lots of long transactions or long high utility item sets the situation may become worse. 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. There are many methods presented for mining the high utility item sets from large transactional datasets are subjected to some serious limitations such as performance of this methods needs to be investigated in low memory based systems for mining high utility itemsets from large transactional datasets and hence needs to address further as well. Another limitation is these proposed methods cannot overcome the screenings as well as overhead of null transactions; hence, performance degrades drastically. During this paper, we are presenting the new approach to overcome these limitations. We presented distributed programming model for mining business-oriented transactional datasets by using an improved Map Reduce framework on Hadoop, which overcomes not only the single processor and main memory-based computing, but also highly scalable in terms of increasing database size. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further. In experimental studies we will compare the performances of existing algorithms UP-Growth and UP-Growth+ against the improve UP-Growth and UP-Growth+ with Hadoop.