Reduced Overestimated Utility and Pruning Candidates using Incremental Mining

Abstract

Objectives: We proposed a Maximum Utility Growth (MUG) algorithm and Maximum Item Quantity (MIQ) Tree structure is used to decrease the number of candidates and reduce the candidate Itemset. Methods: In high utility Itemset mining profit and quantity of items are important in transaction. Several algorithms have proposed to solve the issues of large number of candidate Itemset generation. The proposed MUG algorithm and MIQ-Tree structure is used to reduce candidate Itemset and overestimated utilities in incremental mining. Moreover, the MUG with second strategy of approach calculates maximum utility of each expanded Itemset from the current prefix with supports in mining process. Finding: MUG algorithm and MIQ-Tree are proposed for mining high utility itemset to reduce the overestimated utility and it proposed two strategies for pruning candidate item sets efficiently in the process. It reduces the number of candidate and improves the performance of incremental mining. MIQ-Tree proposed to construct the tree with single-pass. The tree structure can restricted without new database and it employs decreasing the overestimated utility. MUG proposed to prune the number of candidate itemset with two strategies (I) Pruning 1-Itemset Candidates with Real Item Utilities and (II) Pruning Candidates with Estimated Maximum Itemset Utility. The experimental shows that the method developed the performance by decreasing number of candidate itemset. Performance evaluation shows that it reduces the number of candidate and its runtime with equal usage memory. Through the strategy, it can effectively eliminate the search space in the process. Applications: There is large amount of real world application such as retail marketing and stock marketing has emerged techniques high utility Itemset mining.