Performance Comparison of Absolute High Utility Itemset Mining (AHUIM) Algorithm for Big Data
Citation
MLA Style: Sandeep Dalal, Vandna Dahiya "Performance Comparison of Absolute High Utility Itemset Mining (AHUIM) Algorithm for Big Data" International Journal of Engineering Trends and Technology 69.1(2021):17-23.
APA Style:Sandeep Dalal, Vandna Dahiya. Performance Comparison of Absolute High Utility Itemset Mining (AHUIM) Algorithm for Big Data International Journal of Engineering Trends and Technology, 69(1), 17-23.
Abstract
High utility itemset mining (HUI) targets the mining of high utility itemsets from a database. The utility here is defined as the amalgamation of the magnitude of the item and its importance. Although various studies have been done on HUI, they are mainly dedicated to centralized datasets and are not mountable for big data. A novel technique called the Absolute High Utility Itemset Mining (AHUIM) algorithm for parallel mining of HUIs has been recommended to tackle the issue of big data environment. The algorithm uses the Spark-in-memory computing architecture where the whole mining task is divided into smaller independent sub-tasks. Several pruning strategies have been used to implement the algorithm to efficiently mine the dataset, diminishing the need for traversing unpromising search space. The proposed algorithm inherits Spark’s numerous properties such as fault tolerance, scalability, low communication cost, etc. In this research work, the functioning of AHUIM is being evaluated by comparing it with the most recent and fast algorithms for mining HUIs from big data. Extensive experiments show that the novel algorithm is better than other state-of-the-art algorithms for various factors such as time complexity, storage, scalability, etc.
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Keywords
big data mining, distributed computing, MapReduce, Spark platform, utility mining