Affiliation:
1. Bannari Amman Institute of Technology
2. Karpagam College of Engineering
Abstract
Abstract
High Utility Itemset Mining (HUIM) is very crucial mining process in the field of data mining because of its wide range applications apart from market analysis. But HUIM often mines lengthier itemsets as high utility itemset though it is not and the shorter valuable itemsets are left unidentified. High Average Utility Itemset Mining (HAUIM) overcomes the drawback of HUIM and mines the valuable itemsets based on their true values rather than getting affected because of the length or the number of items in the itemset. The proposed algorithm, mines High Average Utility Rare Itemset using the Multi-Objective Evolutionary Algorithm (HAURI-MOEA/D) based on the decomposition technique. Mining rate itemset holds an important insight in many applications like detecting anomalies, market differentiation, healthcare, scientific research and much more. This work aims at mining such unique rate itemsets with high average utility from the uncertain database. The uncertainty in the database here refers to the dynamic nature of the utility associated with each unique item in the dataset. In real world data, the utility of the items will vary time to time and the same has been considered as uncertainty in this work. The proposed algorithm is compared with other multi-objective algorithms to mine rare HAUIs and it is proved that the proposed algorithm performs well in terms of Hypervolume, Coverage and Generational Distance.
Publisher
Research Square Platform LLC
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