Discovering frequent high average utility itemset without transaction insertion

Abstract

Data mining is a technique through which we can find interesting data and sequence from the available wide range of data source. Incremental high-average utility pattern mining (IHAUPM) algorithm is represented to manage the incremental database with transaction insertion. IHAUPM algorithm basically follows the comparison to the original database and newly inserted database itemset if itemset has High Average Utility Upper Bound Itemset (HAUUBI) in the initial database as well as new transaction database then the item always frequent. Second situation itemset has non-High Average Utility Upper Bound Itemset (non-HAUUBI) in the initial database as well as new transaction database then the item always not frequent. Otherwise, the itemset is recurring or not is identified by the given information. This new algorithm is represented in this paper to generate expected high utility frequent item set to form a new transaction database; this algorithm is much faster than the existing algorithm.