Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments
Authors: 
Jerry Chun-Wei Lin, Youcef Djenouri, Gautam Srivastava, 
Philippe Fournier-Viger Academic Editor: Xingsi XueAuthors Info & Claims
Jerry Chun-Wei Lin, Youcef Djenouri, Gautam Srivastava, Philippe Fournier-Viger Academic Editor: Xingsi XueAuthors Info & ClaimsAbstract
In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The k-means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.
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Published: 01 January 2021
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