Novel utility procedure for filtering high associated utility items from transactional databases

Abstract

In data mining, mining and analysis of data from different transactional data sources is an aggressive concept to explore optimal relations between different item sets. In recent years number of algorithms/methods was proposed to mine associated rule based item sets from transactional databases. Mining optimized high utility (like profit) association rule based item sets from transactional databases is still a challenging task in item set extraction in terms of execution time. We propose High Utility based Association Pattern Growth (HUAPG) approach to explore high association utility item sets from transactional data sets based on user item sets. User related item sets to mine associated items using utility data structure (UP-tree) with respect to identification of item sets in proposed approach. Proposed approach performance with compared to hybrid and existing methods worked on synthetic related data sets. Experimental results of proposed approach not only filter candidate item sets and also reduce the run time when database contain high amount of data transactions.