A Novel Approach For Frequent Itemset Mining Using Geometric Progression Number Labeling

Abstract

The task of finding Frequent Item Sets is a very important and time-consuming process in the field of data mining. In the process of Knowledge discovery from data (KDD), the analysis of Frequent Itemset Mining (FIM) produces very use full information like how the items or data are correlated with each other and how frequently it is being found in the database especially in the transaction data, its report plays a vital role for the decision-makers to procure items and to stock it. Since the stock is the asset of the business and the profit of the business depends on the liquidity of the asset, more importance was given to Frequent Itemset mining. In this present Big data era, the variety and volume of data are growing tremendously; it is a challenging situation to mine the valuable information in a faster manner. Even though many researchers proposed many different kinds of algorithms for Frequent Item Set Mining it suffers defeat against either the size of frequent itemset or the time taken to finish the task. From this point of view, this paper proposes a new approach to mine the frequent items by using the Geometric progression series number as a label for each item and making its sum of items in the subset as a single value to find its frequency.